02 Cultivation score weights

Goal

Determine the extent to which each of the data elements related to the PG cultivation score (see Ben Porter’s Donor Cultivation Checklist) have impacted giving.

Setup and datafile

library(tidyverse)
library(gridExtra)
library(e1071)
library(wranglR)
library(foreach)
library(splines)
# Functions adapted from previous analysis steps
source('code/functions.R')

The data file is generated with this code, adapted from the import data step of 01 Initial exploration.Rmd.

filepath <- 'data/2018-07-26 PG scores for all active prospects.xlsx'
source('code/generate-data.R')

Background

The following 12 items were able to be extracted from the database:

• Alum or spouse of alum
• Age
• Chicago home address
• Visited by prospect manager in last 2 years
• 5+ total visits
• Deep engagement (multiple family degrees, parent, season tickets, etc.)
• High-level annual giving ($25K+)
• High-level advisory board participation
• Previous major gift ($250K+)
• Meeting with president
• Open proposal
• Consistent donor (10+ years of giving, including 1+ of last 3)

Each counts as one point toward the cultivation score, which ranges from 0 to 12.

The context for this analysis is to look at how giving is related to the cultivation score. Consider the following two plots (replicated from 01 Initial exploration.Rmd):

There is a nearly linear relationship between PG cultivation score and both campaign giving and an entity’s largest gift or pledge payment. Fitting some sort of linear model will enhance our understanding of the various checklist items by estimating their relative impact.

Data exploration

Indicators

Look at how each of the factors are distributed.

pool %>% select(
  ACTIVE_PROPOSALS, AGE, PM_VISIT_LAST_2_YRS, VISITS_5PLUS, AF_25K_GIFT, GAVE_IN_LAST_3_YRS
  , MG_250K_PLUS, PRESIDENT_VISIT, TRUSTEE_OR_ADVISORY_BOARD, Alumnus, DEEP_ENGAGEMENT
  , CHICAGO_HOME
) %>%
  gather('Variable', 'x', 1:12) %>%
  group_by(Variable) %>%
  summarise(pct.yes = mean(x), yes = sum(x), no = nrow(pool) - sum(x), n = nrow(pool)) %>%
  arrange(desc(pct.yes)) %>%
  mutate(pct.yes = scales::percent(pct.yes))

As expected, alumni status is far and away the leader. I’m a bit surprised that trustee and advisory board is as high as it is. MG and president visit are the least common factors. However, they still have hundreds of observations each so I don’t see a reason to drop either.

Underlying variables

Exploration of the underlying variables for the indicators (that were straightforward to compute). The blue lines indicate the mean, and the purple ones the median.

# Function to produce a summary table, with higher order central moments
summary_moments <- function(x, name = NULL) {
  # Requires e1071 for the skewness and kurtosis functions
  suppressPackageStartupMessages(
    if (!require(e1071)) {
      stop('Requires installation of package e1071')
    }
  )
  # If no name passed use the name of x
  if (is.null(name)) {name <- quote(x) %>% deparse()}
  # Data frame to be returned
  data.frame(
    name = name
    , n = na.omit(x) %>% length()
    , min = min(x, na.rm = TRUE)
    , median = median(x, na.rm = TRUE)
    , mean = mean(x, na.rm = TRUE)
    , max = max(x, na.rm = TRUE)
    , sd = sd(x, na.rm = TRUE)
    , skewness = e1071::skewness(x, na.rm = TRUE)
    , kurtosis = e1071::kurtosis(x, na.rm = TRUE)
    , NAs = is.na(x) %>% sum()
  ) %>% return()
}
# Generic function to create histograms
histogrammer <- function(data, x, binwidth = 1, bins = NULL, color = NULL
                         , trans = 'identity', xbreak = waiver()) {
  vec <- paste0('data$', eval(x)) %>% parse(text = .) %>% eval()
  data %>%
    ggplot(aes_string(x = x, color = color)) +
    geom_histogram(binwidth = binwidth, bins = bins, alpha = .5) +
    geom_density(aes(y = ..count..), alpha = .5) +
    geom_vline(xintercept = mean(vec, na.rm = TRUE), color = 'blue', linetype = 'dashed') +
    geom_vline(xintercept = median(vec, na.rm = TRUE), color = 'purple', linetype = 'dotted') +
    scale_x_continuous(trans = trans, breaks = xbreak)
}

pool %>% filter(!is.na(NUMERIC_AGE)) %>%
  histogrammer(x = 'NUMERIC_AGE', xbreak = seq(0, 200, by = 10)) +
  labs(title = 'Age')

summary_moments(pool$NUMERIC_AGE, 'Age')

The age distribution is moderately right-skewed but looks fine. For a quick fix the NAs could be imputed as the group mean.

pool %>%
  histogrammer(x = 'VISIT_COUNT') +
  labs(title = 'Visit count')

summary_moments(pool$VISIT_COUNT, 'Visits')

It’d be sensible to transform the x axis or otherwise get rid of those outliers.

pool %>%
  histogrammer(x = 'VISIT_COUNT', trans = 'sqrt', binwidth = NULL, bins = 200,
               xbreak = c(seq(0, 10, by = 2), seq(10, 100, by = 10), seq(100, 200, by = 20))) +
  labs(title = 'Sqrt visit, log10 count') +
  scale_y_continuous(trans = 'log10plus1', breaks = 10^(0:20))

Nearly a linear decrease in visit count on a log/sqrt scale - though I’m not sure how to interpret this.

pool %>%
  histogrammer(x = 'YEARS_OF_GIVING') +
  labs(title = 'Years of giving')

summary_moments(pool$YEARS_OF_GIVING, 'Years of giving')

This looks fine. There are more loyal donors than I would’ve thought.

pool %>%
  histogrammer(x = 'YEARS_OF_GIVING_LAST_3') +
  labs(title = 'Years of giving last 3')

summary_moments(pool$YEARS_OF_GIVING_LAST_3, 'Years of giving out of last 3')

Mostly nondonors, unsurprisingly.

pool %>%
  histogrammer(x = 'MG_250K_COUNT') +
  labs(title = 'Count of $250K+ major gifts') +
  scale_y_continuous(trans = 'log10plus1', breaks = 10^(0:5), labels = 10^(0:5))

summary_moments(pool$MG_250K_COUNT, '$250K+ major gifts')

pool %>% group_by(MG_250K_COUNT) %>% mutate(n = 1) %>%
  summarise(
    total = sum(n)
    , proportion = {sum(n) / nrow(pool)} %>% scales::percent()
  )

Extremely few people make a single $250K+ gift, much less multiple ones.

pool %>%
  histogrammer(x = 'SEASON_TICKET_YEARS', xbreak = seq(0, 20, by = 2)) +
  labs(title = 'Years holding season tickets') +
  scale_y_continuous(trans = 'log10plus1', breaks = 10^(0:20))

summary_moments(pool$SEASON_TICKET_YEARS, 'Years holding season tickets')

That jump at 10 years is odd. Perhaps season tickets have only been consistently tracked for about 10 years?

Underlying variable scatterplots

pool %>% filter(!is.na(NUMERIC_AGE)) %>%
  scatterplotter(x = 'NUMERIC_AGE', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(NUMERIC_AGE)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Age versus campaign giving')

pool %>% filter(!is.na(NUMERIC_AGE)) %>%
  scatterplotter(x = 'NUMERIC_AGE', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(NUMERIC_AGE)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Age versus largest gift')

As usual, age is positively associated with giving. The outlier 113-year-old should probably be removed.

max_age <- 110
pool %>% filter(NUMERIC_AGE <= max_age) %>%
  scatterplotter(x = 'NUMERIC_AGE', y = 'LARGEST_GIFT_OR_PAYMENT'
                 , color = 'MG_PR_MODEL_DESC', ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(NUMERIC_AGE)), color = 'blue', linetype = 'dashed') +
  labs(title = bquote('Age versus largest gift' ~ (age <= .(max_age)) ))

For visits, based on the above exploration visit count needs a transformation.

pool %>%
  scatterplotter(x = 'VISIT_COUNT', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(VISIT_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Log-sqrt visit count versus campaign giving') +
  scale_x_sqrt()

pool %>%
  scatterplotter(x = 'VISIT_COUNT', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(VISIT_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Log-sqrt visit count versus largest gift') +
  scale_x_sqrt()

pool %>%
  scatterplotter(x = 'VISIT_COUNT', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(VISIT_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Log-log visit count versus campaign giving') +
  scale_x_continuous(trans = 'log10plus1', breaks = c(0, 1, 10, 50, 100, 150, 200))

pool %>%
  scatterplotter(x = 'VISIT_COUNT', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(VISIT_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Log-log visit count versus largest gift') +
  scale_x_continuous(trans = 'log10plus1', breaks = c(0, 1, 10, 50, 100, 150, 200))

The log-log plots look quite good. Visits \(\geq\) 100 are outliers, but at first glance don’t appear influential on the log-log scale (easy to test with e.g. Cook’s D).

pool %>%
  scatterplotter(x = 'YEARS_OF_GIVING', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(YEARS_OF_GIVING)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Years of giving versus campaign giving') +
  scale_x_sqrt()

pool %>%
  scatterplotter(x = 'YEARS_OF_GIVING', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(YEARS_OF_GIVING)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Years of giving versus largest gift') +
  scale_x_sqrt()

The square root transformation does well for campaign giving, but not as well for largest gift or payment.

# Box-Cox test for transformations
boxcox_lambdas <- seq(-1, 1, by = .01)
boxcox_lm <- lm(I(LARGEST_GIFT_OR_PAYMENT + 1) ~ YEARS_OF_GIVING, data = pool) %>%
  MASS::boxcox(lambda = boxcox_lambdas, plotit = FALSE)
maxLL <- boxcox_lm$x[which(boxcox_lm$y == max(boxcox_lm$y))]
# Plot results
boxcox_lm %>%
  unlist() %>%
  matrix(nrow = length(boxcox_lambdas)) %>%
  data.frame() %>%
  select(x = X1, y = X2) %>%
  ggplot(aes(x = x, y = y)) +
  geom_line() +
  geom_vline(aes(xintercept = x[which(y == max(y))]), color = 'blue', linetype = 'dashed') +
  labs(title = 'Box-Cox analysis', x = expression(lambda), y = 'log Likelihood')

\(\lambda =\) 0.03 is pretty close to a log transformation.

# Best Box-Cox transformation, adding 1 so the response variable is strictly positive
boxcoxbest_trans <- function(x) {
  scales::trans_new(
    'boxcoxbest'
    , transform = function(x) {(x + 1)^maxLL}
    , inverse = function(x) {(x + 1)^(1/maxLL)}
  )
}

grid.arrange(
# Plot Box-Cox results
  pool %>%
    scatterplotter(x = 'YEARS_OF_GIVING', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                   , ytrans = 'log10plus1', ylabels = scales::dollar) +
    geom_vline(aes(xintercept = mean(YEARS_OF_GIVING)), color = 'blue', linetype = 'dashed') +
    labs(title = 'Years of giving versus campaign giving, Box-Cox transformation', y = 'Largest gift') +
    scale_x_continuous(trans = 'boxcoxbest', breaks = c(0, 10))
# Plot log10 results
  , pool %>%
    scatterplotter(x = 'YEARS_OF_GIVING', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                   , ytrans = 'log10plus1', ylabels = scales::dollar) +
    geom_vline(aes(xintercept = mean(YEARS_OF_GIVING)), color = 'blue', linetype = 'dashed') +
    labs(title = 'Years of giving versus campaign giving, log transformation', y = 'Largest gift') +
    scale_x_continuous(trans = 'log10plus1')
)

After all that neither look linear, though they do look nearly linear around the mean.

pool %>% mutate(last_3_yrs = factor(YEARS_OF_GIVING_LAST_3)) %>%
  ggplot(aes(x = last_3_yrs, y = CAMPAIGN_NEWGIFT_CMIT_CREDIT, color = MG_PR_MODEL_DESC)) +
  geom_boxplot() +
  facet_grid(. ~ MG_PR_MODEL_DESC) +
  scale_y_continuous(trans = 'log10plus1', labels = scales::dollar, breaks = 10^(0:20)) +
  labs(title = 'Years of giving of last 3 versus campaign giving')

pool %>% mutate(last_3_yrs = factor(YEARS_OF_GIVING_LAST_3)) %>%
  ggplot(aes(x = last_3_yrs, y = LARGEST_GIFT_OR_PAYMENT, color = MG_PR_MODEL_DESC)) +
  geom_boxplot() +
  facet_grid(. ~ MG_PR_MODEL_DESC) +
  scale_y_continuous(trans = 'log10plus1', labels = scales::dollar, breaks = 10^(0:20)) +
  labs(title = 'Years of giving of last 3 versus largest gift')

I see a main effect for campaign giving.

pool %>%
  scatterplotter(x = 'MG_250K_COUNT', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(MG_250K_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Count of $250K+ gifts versus campaign giving') +
  scale_x_sqrt()

pool %>%
  scatterplotter(x = 'MG_250K_COUNT', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(MG_250K_COUNT)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Count of $250K+ gifts versus largest gift') +
  scale_x_sqrt()

As seen above, pretty much all of the observations are outliers. Might make sense to leave this one discretized.

pool %>%
  scatterplotter(x = 'SEASON_TICKET_YEARS', y = 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(SEASON_TICKET_YEARS)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Season ticket years versus campaign giving') +
  scale_x_continuous(breaks = seq(0, 100, by = 2))

pool %>%
  scatterplotter(x = 'SEASON_TICKET_YEARS', y = 'LARGEST_GIFT_OR_PAYMENT', color = 'MG_PR_MODEL_DESC'
                 , ytrans = 'log10plus1', ylabels = scales::dollar) +
  geom_vline(aes(xintercept = mean(SEASON_TICKET_YEARS)), color = 'blue', linetype = 'dashed') +
  labs(title = 'Season ticket years versus largest gift') +
  scale_x_continuous(breaks = seq(0, 100, by = 2))

This looks fine.

Data exploration takeaways

• Missing age values can be imputed as mean age
• The 113-year-old is an outlier
• Visit count could use log transform
• Years of giving could use sqrt transform
• $250K+ gifts is a binary indicator

Modeling ground rules

I’ll outline some basic methodology in advance of modeling to try to account for researcher degrees of freedom.

As seen above, both campaign giving and largest lifetime gift have a strong linear relationship with the cultivation score, formulated as a 0 to 12 point scale. Currently, all criteria count as a single point (equally weighted) but I suspect they impact actual giving behavior differently. I propose four models:

1. Linear regression of campaign giving on cultivation score variables
2. Linear regression of campaign on all available explanatory variables, essentially looking at the cultivation score variables controlling for the preexisting modeled scores
3. Linear regression of largest cash transaction on cultivation score variables
4. Linear regression of largest cash transaction on all available explanatory variables, again to control for the preexisting modeled scores

If a given explanatory variable has the same coefficient sign and similar nonzero magnitudes in all models, I’ll take this as evidence of an association with giving behavior.

After withholding a random 20% of the data as a test set, I’ll use ten-fold cross-validation to determine which variables should be included in each model. Model performance will be determined based on out-of-sample mean squared error, defined as usual:

\[ {\text{MSE}} = \frac{1}{n} \sum_{i=1}^{n} \left( Y_i - \hat{Y}_i \right)^2 \]

The 20% test set will be used to confirm the cross-validation results, and then final models will be constructed on the entire dataset for comparison.

Data cleanup

First, perform some quick data clean-up.

mdat <- pool %>% mutate(
  # Impute missing ages as mean age
  NUMERIC_AGE = case_when(
    !is.na(NUMERIC_AGE) ~ NUMERIC_AGE
    , TRUE ~ mean(NUMERIC_AGE, na.rm = TRUE)
  )
  # Impute missing affinity scores as mean affinity score
  , AFFINITY_SCORE = case_when(
    !is.na(AFFINITY_SCORE) ~ AFFINITY_SCORE
    , TRUE ~ mean(AFFINITY_SCORE, na.rm = TRUE)
  )
  # Create null factor levels for the MG_ID and MG_PR models
  , MG_ID_MODEL_DESC = fct_explicit_na(MG_ID_MODEL_DESC, 'Unscored') %>% fct_relevel('Unscored')
  , MG_PR_MODEL_DESC = fct_explicit_na(MG_PR_MODEL_DESC, 'Unscored') %>% fct_relevel('Unscored')
  # Create row numbers
  , rownum = 1:nrow(pool)
) %>% select(
  # Drop unhelpful fields
  -ID_NUMBER, -PROSPECT_ID, -PROSPECT_NAME, -NU_DEG, -NU_DEG_SPOUSE, -POTENTIAL_INTEREST_AREAS
  , -PREF_NAME_SORT, -MG_ID_MODEL_YEAR, -MG_ID_MODEL_SCORE, -MG_PR_MODEL_YEAR, -MG_PR_MODEL_SCORE
)

I’ll use my wranglR::KFoldXVal function to create the cross-validation groups.

k <- 11
# Create k groups: first is 20% of the data (prop = .2) and the others are equally sized
xval_inds <- pool %>% wranglR::KFoldXVal(k = k, prop = .2, seed = 12644)
# Remove outliers
outliers <- which(mdat$NUMERIC_AGE > max_age)
for (i in 1:k) {
  rm_idx <- which(xval_inds[[i]] %in% outliers)
  if (length(rm_idx) > 0) {xval_inds[[i]] <- xval_inds[[i]][-rm_idx]}
}
# Create groups
oos_inds <- xval_inds[[1]]
xval_inds <- xval_inds[2:k]
# Results
c(list(oos_inds), xval_inds) %>% summary

      Length Class  Mode   
 [1,] 5215   -none- numeric
 [2,] 2086   -none- numeric
 [3,] 2086   -none- numeric
 [4,] 2086   -none- numeric
 [5,] 2086   -none- numeric
 [6,] 2086   -none- numeric
 [7,] 2086   -none- numeric
 [8,] 2086   -none- numeric
 [9,] 2086   -none- numeric
[10,] 2086   -none- numeric
[11,] 2090   -none- numeric

Group 1 is the out-of-sample validation set, and the other 10 will be used for cross-validation.

Finally, I’ll create a quick function to compute MSE.

calc_mse <- function(y, yhat) {
  mean(
    (y - yhat)^2, na.rm = TRUE
  )
}

Visualization functions

# Create coefficients data frame
create_coefs <- function(model_list) {
  foreach(i = 1:length(model_list), .combine = 'rbind') %do% {
    tmp <- summary(model_list[[i]])$coefficients
    data.frame(tmp) %>%
    mutate(
      variable = rownames(tmp)
      , model = i
    ) %>% select(
      model
      , variable
      , beta.hat = Estimate
      , SE = Std..Error
      , t.val = t.value
      , Pr.t = Pr...t..
    ) %>% return()
  } %>% return()
}
# Plot R-squared
plot_r2 <- function(model_list, type = 'r.squared') {
  parser <- function(x) {
    tmpsum <- summary(x)
    paste0('tmpsum$', type) %>% parse(text = .) %>% eval() %>% return()
  }
  model_list %>%
  lapply(., function(x) parser(x)) %>% unlist() %>% data.frame(r.squared = .) %>%
    ggplot(aes(x = r.squared)) + 
    geom_density() +
    geom_vline(aes(xintercept = mean(r.squared)), color = 'blue', linetype = 'dashed', alpha = .5) +
    geom_rug(color = 'blue') +
    labs(title = bquote('Density plot of' ~ r^2 ~ 'results, mean' ~
        .(lapply(model_list, function(x) {summary(x)$r.squared}) %>% unlist() %>% mean() %>% round(3))
      )
      , x = bquote(r^2)
    )
}
# Plot cross-validated coefficients
plot_coefs <- function(model_list, conf_interval = 1 - p.sig) {
  crit_val <- qnorm({1 - conf_interval} / 2) %>% abs()
  coefs <- create_coefs(model_list)
  coefs %>% full_join(
    coefs %>% group_by(variable) %>%
      summarise(group.mean = mean(beta.hat), group.sd = sd(beta.hat))
    , by = c('variable', 'variable')
  ) %>%
  ggplot(aes(x = variable, y = beta.hat, color = factor(model))) +
  geom_segment(
    aes(
      xend = variable
      , y = group.mean - 2 * crit_val * group.sd
      , yend = group.mean + 2 * crit_val * group.sd
    ), color = 'gray', alpha = .25, size = 2) +
  geom_point() +
  geom_hline(yintercept = 0, alpha = .5) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5), axis.title.y = element_text(angle = 0, vjust = .5)) +
  labs(
    title = 'Coefficient estimates per cross-validation model'
    , y = bquote(hat(beta))
    , color = 'cross-validation sample'
  )
}
# Table of coefficient +/- counts
coef_pm_table <- function(model_list, pval) {
  create_coefs(model_list) %>%
  group_by(variable) %>%
  summarise(
    `+` = sum(sign(beta.hat) == 1 & Pr.t < pval)
    , `0` = sum(Pr.t >= pval)
    , `-` = sum(sign(beta.hat) < 0 & Pr.t < pval)
  )
}
# Compute predictions
calc_preds <- function(model_list, xval, yname) {
  yhats <- list()
  for (i in 1:length(model_list)) {
    yhats[[i]] <- data.frame(
      model = i
      , row = xval[[i]]
      , preds = model_list[[i]] %>% predict(newdata = mdat[xval[[i]], ])
      , truth = mdat[xval[[i]], yname] %>% unlist() %>% log10plus1()
    )
  }
  return(yhats)
}
calc_outsample_mse <- function(model_list, xval, yname) {
  calc_preds(model_list, xval, yname) %>%
    lapply(function(x) calc_mse(y = x$truth, yhat = x$preds)) %>%
    unlist()
}
# Plot MSEs by insample/outsample
plot_mses <- function(model_list, xval, truth) {
  mses <- data.frame(
    insample = model_list %>%
      lapply(function(x) calc_mse(y = model.frame(x)[, 1], yhat = predict(x))) %>%
      unlist()
    , outsample = calc_outsample_mse(model_list, xval, truth)
  ) %>% gather('type', 'MSE', 1:2)
  mses %>%
    ggplot(aes(x = MSE, color = type)) +
    geom_density() +
    geom_vline(
      xintercept = mses %>% filter(type == 'insample') %>% select(MSE) %>% unlist %>% mean()
      , color = 'red', linetype = 'dashed', alpha = .5
    ) +
    geom_vline(
      xintercept = mses %>% filter(type == 'outsample') %>% select(MSE) %>% unlist %>% mean()
      , color = 'darkcyan', linetype = 'dashed', alpha = .5
    ) +
    geom_rug() +
    labs(
      title = bquote('MSE across samples, means =' ~
          .(mses %>% group_by(type) %>% summarise(mean = mean(MSE)) %>%
              select(mean) %>% unlist() %>% round(3) %>% paste(collapse = ', ')
          )
        )
    )
}
# Merges predicted results into one large data frame each for insample and outsample
calc_resids <- function(model_list, xval, yname) {
  insample <- foreach(i = 1:length(model_list), .combine = 'rbind') %do% {
    data.frame(
      model = i
      , preds = model_list[[i]] %>% predict()
      , truth = model.frame(model_list[[i]])[, 1]
    ) %>% mutate(
      residuals = truth - preds
    )
  }
  preds <- calc_preds(model_list, xval, yname)
  outsample <- foreach(i = 1:length(model_list), .combine = 'rbind') %do% {
    preds[[i]]
  } %>% mutate(
    residuals = truth - preds
  )
  return(list(insample = insample, outsample = outsample))
}
# Plot standardized residuals; returns a list of ggplot objects $insample and $outsample
plot_resids <- function(model_list, xval, yname, filter = 'TRUE') {
  resids <- calc_resids(model_list, xval, yname)
  # Plot residuals vs fitted for in-sample data
  insample <- resids$insample %>% filter_(filter) %>%
    ggplot(aes(x = preds, y = residuals, color = factor(model))) +
    geom_point(alpha = .01) +
    geom_smooth(se = FALSE) +
    labs(title = 'In-sample residuals versus fitted', color = 'cross-validation sample')
  # Plot residuals vs fitted for out-of-sample data
  outsample <- resids$outsample %>% filter_(filter) %>%
    ggplot(aes(x = preds, y = residuals, color = factor(model))) +
    geom_point(alpha = .1) +
    geom_smooth(se = FALSE) +
    labs(title = 'Out-of-sample residuals versus fitted', color = 'cross-validation sample')
  return(list(insample = insample, outsample = outsample))
}
# Plot normal Q-Q visualization for residuals
plot_qq <- function(model_list, xval, yname, filter = 'TRUE') {
  resids <- calc_resids(model_list, xval, yname)
  # In-sample Q-Q plot with standardized residuals
  insample <- resids$insample %>% mutate(st.resid = residuals/sd(residuals)) %>% filter_(filter) %>%
    ggplot(aes(sample = st.resid, color = factor(model))) +
    geom_qq(alpha = .05) +
    geom_qq_line() +
    labs(title = 'In-sample Q-Q plot with standardized residuals'
         , color = 'cross-validation sample')
  # Out-of-sample Q-Q plot
  outsample <- resids$outsample %>% mutate(st.resid = residuals/sd(residuals)) %>% filter_(filter) %>%
    ggplot(aes(sample = st.resid, color = factor(model))) +
    geom_qq(alpha = .05) +
    geom_qq_line() +
    labs(title = 'Out-of-sample Q-Q plot with standardized residuals'
         , color = 'cross-validation sample')
  return(list(insample = insample, outsample = outsample))
}

Campaign linear models

Cultivation score predictors

Regress campaign giving against each of the cultivation score predictors.

# List to store campaign linear models
clms <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  clms[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~
        ACTIVE_PROPOSALS +
        AGE +
        PM_VISIT_LAST_2_YRS +
        VISITS_5PLUS +
        AF_25K_GIFT +
        GAVE_IN_LAST_3_YRS +
        MG_250K_PLUS +
        PRESIDENT_VISIT +
        TRUSTEE_OR_ADVISORY_BOARD +
        Alumnus +
        DEEP_ENGAGEMENT +
        CHICAGO_HOME
      , data = .
    )
}

The full (and hard to read) results for each model are in the appendix.

We can extract a few parameters of interest.

plot_r2(clms) +
  geom_text(y = seq(10, 100, length.out = k - 1), label = 1:(k - 1), color = 'blue') +
  xlim(c(.45, .48))

The average \(r^2 =\) 0.465 is quite a good result.

p.sig <- 1E-2
plot_coefs(clms)

coef_pm_table(clms, p.sig)

The coefficients are extremely tightly clustered within each cross-validation set. Interestingly, age and alumni status both have negative coefficients. All are significant at \(p =\) 0.01.

Here are the actual prediction MSEs:

plot_mses(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')

As usual, in-sample performance is moderately optimistic.

plot_resids(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$insample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

plot_resids(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

At first glance, the linear trend evident in these results is concerning. Upon second glance, while still sobering it really just reinforces what we’d observed in the first of the two plots in the Background section: campaign giving is decidedly nonlinear as cultivation scores approach their high/low limits. As seen above, the underlying factors should be transformed, and possibly modeled nonlinearly (splines).

plot_qq(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$insample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

plot_qq(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

The quantile-quantile plot further illustrates the issue. The drift above the reference line to the left and below it to the right suggests less density than expected in the tails, which follows given that the range is bound – it’s not possible to give less than a nondonor or (for practical purposes) more than a 9-figure donor.

The story is completely different when looking only at those who actually gave:

plot_qq(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', filter = 'truth > 0')$insample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

plot_qq(clms, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT', filter = 'truth > 0')$outsample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

This is quite a bit nicer, suggesting only slight skewness in the tails.

Takeaway. When modeling giving amounts in the future, consider fitting a conditional model. Something along the lines of:

\[ E(\text{giving amount} ~ | ~ \text{donor status} = 1) \]

All predictors 1

The second model compares the variables underlying each PG score indicator, plus supplemental predictors. The first model incudes everything and I’ll prune from there using MSE. Initial spline df are fairly arbitrary.

# List to store campaign linear models
clmaps <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  clmaps[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~
        ACTIVE_PROPOSALS +
        ns(NUMERIC_AGE, df = 5) + # Underlying variable to AGE indicator
        PM_VISIT_LAST_2_YRS +
        log10plus1(VISIT_COUNT) + # Underlying VISITS_5PLUS indicator
        AF_25K_GIFT +
        sqrt(YEARS_OF_GIVING) + # Underlying GAVE_IN_LAST_3_YRS
        ns(YEARS_OF_GIVING_LAST_3, df = 2) + # Underlying GAVE_IN_LAST_3_YRS
        MG_250K_PLUS + # Decided to leave as factor
        PRESIDENT_VISIT +
        TRUSTEE_OR_ADVISORY_BOARD +
        Alumnus +
        DOUBLE_ALUM + # Deep Engagement component
        EVER_PARENT + # Deep Engagement component
        ns(SEASON_TICKET_YEARS, df = 1) + # Deep Engagement component
        CHICAGO_HOME +
        QUAL_LEVEL +
        AFFINITY_SCORE +
        MG_PR_MODEL_DESC
      , data = .
    )
}

Full results are in the appendix.

plot_r2(clmaps) +
  geom_text(y = seq(10, 150, length.out = k - 1), label = 1:(k-1), color = 'blue') +
  xlim(c(.72, .735))

This is a much higher \(r^2\) than seen above, but the model also includes many more predictors. Consider the MSE.

plot_mses(clmaps, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')

Well, that’s pretty conclusive – this is also much lower than that seen previously. This implies that on average the predicted giving amount is less than a factor of 10 off. Which predictors contribute to the performance?

plot_coefs(clmaps)

coef_pm_table(clmaps, p.sig)

After accounting for the other variables, things that don’t seem to matter include active proposals, Chicago home address, two of the deep engagement indicators, president visits, qualification level (inconsistent), and committee participation. Note that the “Future Prospect” factor level only appears 9 times – one of the cross-validation samples must not have had anyone rated at that level. Additionally, some of these are likely already included in the affinity score.

plot_resids(clmaps, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2))

plot_qq(clmaps, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample 

This already looks a lot nicer than the Cultivation score predictors model! Now let’s try again, dropping the less-interesting predictors.

All predictors 2

Full results are in the appendix.

# List to store campaign linear models
clmaps2 <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  clmaps2[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~
        ns(NUMERIC_AGE, df = 5) + # Underlying variable to AGE indicator
        PM_VISIT_LAST_2_YRS +
        log10plus1(VISIT_COUNT) + # Underlying VISITS_5PLUS indicator
        AF_25K_GIFT +
        sqrt(YEARS_OF_GIVING) + # Underlying GAVE_IN_LAST_3_YRS
        ns(YEARS_OF_GIVING_LAST_3, df = 2) + # Underlying GAVE_IN_LAST_3_YRS
        MG_250K_PLUS + # Decided to leave as factor
        Alumnus +
        ns(SEASON_TICKET_YEARS, df = 1) + # Deep Engagement component
        AFFINITY_SCORE +
        MG_PR_MODEL_DESC
      , data = .
    )
}

plot_mses(clmaps2, xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')

That’s a marginal increase in outsample MSE (0.9211 vs. 0.9316, difference of 1.14%) with many fewer predictors.

plot_coefs(clmaps2)

coef_pm_table(clmaps2, p.sig)

The spline degrees of freedom for numeric age could be tweaked.

All predictors splines test

splines_max <- 10
clmaps3 <- list()
for (s in 1:splines_max) {
  clmaps3[[s]] <- list()
  for(i in 1:length(xval_inds)) {
    # Create linear model excluding the holdout and out-of-sample indices
    clmaps3[[s]][[i]] <- mdat %>%
      filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
      lm(
        log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~
          ns(NUMERIC_AGE, df = s) + # Underlying variable to AGE indicator
          PM_VISIT_LAST_2_YRS +
          log10plus1(VISIT_COUNT) + # Underlying VISITS_5PLUS indicator
          AF_25K_GIFT +
          sqrt(YEARS_OF_GIVING) + # Underlying GAVE_IN_LAST_3_YRS
          ns(YEARS_OF_GIVING_LAST_3, df = 2) + # Underlying GAVE_IN_LAST_3_YRS
          MG_250K_PLUS + # Decided to leave as factor
          Alumnus +
          ns(SEASON_TICKET_YEARS, df = 1) + # Deep Engagement component
          AFFINITY_SCORE +
          MG_PR_MODEL_DESC
        , data = .
      )
  }
}

Try different spline degrees of freedom for NUMERIC_AGE. Full results are in the appendix.

Consider the distribution of MSEs for each model.

# Calculate MSEs for each model
spline_mse <- foreach(s = 1:splines_max, .combine = rbind) %do% {
  mses <- calc_outsample_mse(clmaps3[[s]], xval_inds, 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')
  data.frame(spline.df = s, xv_group = factor(1:(k-1)), mses)
}

# Plot results
spline_mse %>%
  ggplot(aes(x = spline.df, y = mses)) +
  geom_point(aes(color = xv_group)) +
  geom_smooth() +
  scale_x_continuous(breaks = 1:splines_max, minor_breaks = NULL) +
  labs(x = 'spline df', y = 'MSE', color = 'cross-validation sample')

For practical purposes there’s not much difference between the different choices. It looks like 4 or 5 is where the mean MSE levels out, so I’ll stick with my initial choice.

Comparison

Create the two final models and check them on out-of-sample data.

# Predict campaign giving, PG score indicators
clm_final <- clms[[1]] %>% update(data = mdat %>% filter(rownum %in% unlist(xval_inds)))
# Predict campaign giving, underlying factors
clmap_final <- clmaps2[[1]] %>% update(data = mdat %>% filter(rownum %in% unlist(xval_inds)))

Full results in the appendix

plot_resids(list(clmap_final, clm_final), list(oos_inds, oos_inds), 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample +
  scale_color_discrete(labels = c('All predictors', 'PG indicators')) +
  labs(color = 'Model')

The “All predictors” residuals look better than the “PG indicators”" residuals.

plot_qq(list(clmap_final, clm_final), list(oos_inds, oos_inds), 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')$outsample +
  scale_color_discrete(labels = c('All predictors', 'PG indicators')) +
  labs(color = 'Model')

Note the reference lines – “All predictors” is much closer to a normal distribution, albeit with some positive skewness.

c_mses_final <- rbind(
  calc_preds(list(clm_final), list(oos_inds), 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')[[1]] %>%
    mutate(model = 'PG indicators')
  , calc_preds(list(clmap_final), list(oos_inds), 'CAMPAIGN_NEWGIFT_CMIT_CREDIT')[[1]] %>%
    mutate(model = 'All predictors')
) %>% mutate(
  model = factor(model)
  , sq.error = (truth - preds)^2
)
c_mses_final <- c_mses_final %>% left_join(
  c_mses_final %>% group_by(model) %>% summarise(mse = mean(sq.error))
  , by = c('model', 'model')
)
c_mses_final %>%
  ggplot(aes(x = sq.error, color = model)) +
  geom_density() +
  geom_vline(aes(xintercept = mean(sq.error)), linetype = 'dotted', alpha = .5) +
  geom_vline(aes(xintercept = mse, color = model), linetype = 'dashed') +
  xlim(c(0, 5)) +
  labs(
    x = 'squared error'
    , title = bquote('Squared error across models, means = ' ~
        .(c_mses_final %>% group_by(model) %>% summarise(mse = mean(mse)) %>% select(mse) %>%
            unlist() %>% round(3) %>% paste(collapse = ', ')))
  )

We never want to see bimodal squared errors – it looks like “All predictors” is a much better regression model. These are all very close to the results seen in the corresponding sections above, which is reassuring.

Finally, let’s look at the impact of age in the splines model. I’ll try plotting against both the raw \(\textbf{y}\) and the partial residuals, which are obtained by removing the effects of all the predictors besides the \(\textbf{x}_j\) we’re interested in, e.g.

\[ \boldsymbol{\hat{\epsilon}} = \textbf{y} - \hat{\textbf{y}} = \textbf{y} - \sum_{i} {\textbf{x}_i \hat{\boldsymbol{\beta}}_i} \] \[ \boldsymbol{\hat{\epsilon}}_\text{partial} = \textbf{y} - \sum_{i \neq j} {\textbf{x}_i \hat{\boldsymbol{\beta}}_i} \]

clmap_splines_dat <- data.frame(
  age = mdat %>% filter(rownum %in% unlist(xval_inds)) %>% select(NUMERIC_AGE) %>% unlist()
  , y = model.frame(clmap_final)[, 1]
  , y.hat = update(clmap_final, formula = . ~ ns(NUMERIC_AGE, df = 5)) %>% fitted()
  # Compute partial residuals
  , y.partial.resid = {clmap_final %>% residuals(type = 'partial')}[, 'ns(NUMERIC_AGE, df = 5)']
) %>% mutate(
  # Regression on partial residuals
  y.hat.partial = lm(y.partial.resid ~ ns(age, 5)) %>% fitted()
)

clmap_splines_dat %>%
  ggplot(aes(x = age, y = y)) +
  geom_point(alpha = .1, size = 1) +
  geom_hline(yintercept = 2, color = 'darkgray') +
  geom_line(aes(y = y.hat), color = 'red') +
  labs(title = 'Numeric age spline versus campaign giving', y = bquote(log[10] ~ 'campaign giving'))

clmap_splines_dat %>%
  ggplot(aes(x = age, y = y.partial.resid)) +
  geom_point(alpha = .1, size = 1) +
  geom_hline(yintercept = 0, color = 'darkgray') +
  geom_line(aes(y = y.hat.partial), color = 'red') +
  labs(title = 'Numeric age spline versus partial residuals for campaign giving'
       , y = 'partial residuals')

Recall that in the PG indicators model, age had a negative coefficient. This paints a more nuanced picture. The first plot regresses campaign giving on the natural spline of age and shows a dip in expected giving for 60-year-olds, a gradual increase to age 80 or so, and then a slow decline. However, we know from previous experience that age is closely correlated with many other predictors of giving (years of giving, total giving, capacity evaluation, and so on). The partial residual plot corrects for this by removing the effect of all the other predictors and regressing the resulting residuals on age. Now, controlling for the other variables, expected campaign giving appears to reach its maximum in the late 40s or early 50s, and decreases thereafter.

Thoughts

Compare the coefficients for both models.

full_join(
  data.frame(var = coef(clm_final) %>% names, pg.inds.model = coef(clm_final))
  , data.frame(var = coef(clmap_final) %>% names, all.predictors.model = coef(clmap_final))
) %>% mutate(
  # Factor levels in order we want them to appear in below table
  var = factor(var, levels = c('(Intercept)', 'ACTIVE_PROPOSALS', 'AGE', 'ns(NUMERIC_AGE, df = 5)1'
    , 'ns(NUMERIC_AGE, df = 5)2', 'ns(NUMERIC_AGE, df = 5)3', 'ns(NUMERIC_AGE, df = 5)4'
    , 'ns(NUMERIC_AGE, df = 5)5', 'PRESIDENT_VISIT', 'PM_VISIT_LAST_2_YRS', 'VISITS_5PLUS'
    , 'log10plus1(VISIT_COUNT)', 'AF_25K_GIFT', 'GAVE_IN_LAST_3_YRS', 'sqrt(YEARS_OF_GIVING)'
    , 'ns(YEARS_OF_GIVING_LAST_3, df = 2)1', 'ns(YEARS_OF_GIVING_LAST_3, df = 2)2', 'MG_250K_PLUS'
    , 'TRUSTEE_OR_ADVISORY_BOARD', 'Alumnus', 'DEEP_ENGAGEMENT', 'CHICAGO_HOME', 'ns(SEASON_TICKET_YEARS, df = 1)'
    , 'AFFINITY_SCORE', 'MG_PR_MODEL_DESCBottom Tier', 'MG_PR_MODEL_DESCMiddle Tier', 'MG_PR_MODEL_DESCTop Tier'))
) %>% arrange(var)

• The intercept for the all predictors model is much closer to 0. The relatively large intercept for the indicators model likely contributes to its odd residuals behavior (recall the bimodal squared errors).
• Age has a nonlinear relationship with giving. The oldest donors are expected to give slightly less.
• The impact of visits is much smaller for the all predictors model, though more visit activity is always associated with greater giving.
• Giving appears to have a comparable impact between the two models, though interpretation is tricky given that the all predictors model splits its impact over more predictors.
• Alumni status leads to less predicted giving in both models! While surprising on the face of it, this actually fits with what I’ve observed in other datasets. Consider that nonalumni are often added to the database only once they’ve already made a gift, demonstrating both affinity and philanthropic interest.
• The affinity score and MG prioritization models work!

Overall, with Campaign giving as \(y\), several of the PG model variables do seem to offer predictive power above and beyond whatever factors are rolled into the affinity score and MG prioritization models. In particular, age, PM visits, total visits, $25K+ AF gifts, total years of giving, MG gifts, alumni status, and season tickets were still statistically significant when including the other modeled scores. It may be worthwhile paying special attention to these characteristics.

Transaction linear models

Repeat the above, but let \(y_i\) be transformed largest gift or payment.

Cultivation score predictors

Full summary in the appendix.

# List to store transaction linear models
tlms <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  tlms[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(LARGEST_GIFT_OR_PAYMENT) ~
        ACTIVE_PROPOSALS +
        AGE +
        PM_VISIT_LAST_2_YRS +
        VISITS_5PLUS +
        AF_25K_GIFT +
        GAVE_IN_LAST_3_YRS +
        MG_250K_PLUS +
        PRESIDENT_VISIT +
        TRUSTEE_OR_ADVISORY_BOARD +
        Alumnus +
        DEEP_ENGAGEMENT +
        CHICAGO_HOME
      , data = .
    )
}

plot_r2(tlms) +
  geom_text(y = seq(10, 200, length.out = k - 1), label = 1:(k - 1), color = 'blue') +
  xlim(c(.325, .335))

Here, the mean \(r^2 =\) 0.331 is a bit lower than that seen in the campaign models.

plot_coefs(tlms)

coef_pm_table(tlms, p.sig)

Interestingly, all the coefficients are positive besides alumni status, which is not predictive! President visits are hit or miss, which follows given that the quality of visit data is time dependent while gift data is not. The rest all have positive coefficients.

plot_mses(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')

This is actually much lower than what was seen in the campaign cultivation score model.

plot_resids(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$insample

plot_resids(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

This exhibits a similar downward trend as scene in the campaign cultivation score model, so I don’t expect the Q-Q plots to be acceptable.

plot_qq(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$insample

plot_qq(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

That looks almost like a discontinuity at -1.

mdat %>% mutate(x.trans = log10plus1(LARGEST_GIFT_OR_PAYMENT)) %>%
  ggplot(aes(x = x.trans)) +
  geom_histogram(alpha = .5, binwidth = .1) +
  geom_density(aes(y = ..count..)) +
  geom_vline(aes(xintercept = mean(x.trans)), color = 'blue', linetype = 'dashed') +
  geom_vline(aes(xintercept = mean(x.trans) - sd(x.trans)), color = 'blue', linetype = 'dotted')

As before, the combination of binary indicators and all those 0 observations seem to do a number on the model.

plot_qq(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT', filter = 'truth > 0')$insample

plot_qq(tlms, xval_inds, 'LARGEST_GIFT_OR_PAYMENT', filter = 'truth > 0')$outsample

But once again, the expected value conditioned on being a donor is nearly normal. This approach, \(E(\text{giving} ~ | ~ \text{donor status} = 1)\), is definitely worth considering in the future.

All predictors 1

Repeat the above with the underlying variables. Again, the first model will include all variables and I’ll prune it back according to MSE and variable significance. Spline df are set as in the corresponding campaign model.

Full summary in the appendix.

# List to store transaction linear models
tlmaps <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  tlmaps[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(LARGEST_GIFT_OR_PAYMENT) ~
        ACTIVE_PROPOSALS +
        ns(NUMERIC_AGE, df = 5) + # Underlying variable to AGE indicator
        PM_VISIT_LAST_2_YRS +
        log10plus1(VISIT_COUNT) + # Underlying VISITS_5PLUS indicator
        AF_25K_GIFT +
        sqrt(YEARS_OF_GIVING) + # Underlying GAVE_IN_LAST_3_YRS
        ns(YEARS_OF_GIVING_LAST_3, df = 2) + # Underlying GAVE_IN_LAST_3_YRS
        MG_250K_PLUS + # Decided to leave as factor
        PRESIDENT_VISIT +
        TRUSTEE_OR_ADVISORY_BOARD +
        Alumnus +
        DOUBLE_ALUM + # Deep Engagement component
        EVER_PARENT + # Deep Engagement component
        ns(SEASON_TICKET_YEARS, df = 1) + # Deep Engagement component
        CHICAGO_HOME +
        QUAL_LEVEL +
        AFFINITY_SCORE +
        MG_PR_MODEL_DESC
      , data = .
    )
}

plot_r2(tlmaps) +
  geom_text(y = seq(10, 250, length.out = k - 1), label = 1:(k-1), color = 'blue') +
  xlim(c(.593, .605))

plot_mses(tlmaps, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')

While \(r^2\) is not as high as that achieved by the campaign all predictors model, this is the lowest MSE seen yet.

plot_coefs(tlmaps)

coef_pm_table(tlmaps, p.sig)

Intersetingly, there are some notable differences compared to campaign giving. Here, having a Chicago home address does matter, as do parent affinity, and committee participation. Qualification level is still inconsistent, and age appears to matter much less here compared to the campaign model. Interestingly, compared to the previous model, alumni status is significant again once these underlying factors are included.

plot_resids(tlmaps, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

plot_qq(tlmaps, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

That residuals plot is still quite poor, but the Q-Q plot is respectable.

All predictors 2

Re-fit the above, dropping qualification level, active proposals, and double alum. It also appears that fewer splie df for age are called for.

Full results in the appendix.

# List to store transaction linear models
tlmaps2 <- list()
for(i in 1:length(xval_inds)) {
  # Create linear model excluding the holdout and out-of-sample indices
  tlmaps2[[i]] <- mdat %>%
    filter(rownum %in% unlist(xval_inds)[unlist(xval_inds) %nin% xval_inds[[i]]]) %>%
    lm(
      log10plus1(LARGEST_GIFT_OR_PAYMENT) ~
        ns(NUMERIC_AGE, df = 3) + # Underlying variable to AGE indicator
        PM_VISIT_LAST_2_YRS +
        log10plus1(VISIT_COUNT) + # Underlying VISITS_5PLUS indicator
        AF_25K_GIFT +
        sqrt(YEARS_OF_GIVING) + # Underlying GAVE_IN_LAST_3_YRS
        ns(YEARS_OF_GIVING_LAST_3, df = 2) + # Underlying GAVE_IN_LAST_3_YRS
        MG_250K_PLUS + # Decided to leave as factor
        PRESIDENT_VISIT +
        TRUSTEE_OR_ADVISORY_BOARD +
        Alumnus +
        EVER_PARENT + # Deep Engagement component
        ns(SEASON_TICKET_YEARS, df = 1) + # Deep Engagement component
        CHICAGO_HOME +
        AFFINITY_SCORE +
        MG_PR_MODEL_DESC
      , data = .
    )
}

plot_r2(tlmaps2) +
  geom_text(y = seq(10, 250, length.out = k - 1), label = 1:(k-1), color = 'blue') +
  xlim(c(.59, .60))

plot_mses(tlmaps2, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')

Similar \(r^2\) and error, so the smaller model is preferred.

plot_coefs(tlmaps2)

coef_pm_table(tlmaps2, p.sig)

Alumni and parent affiliation reduce the expected largest gift amount, on average.

plot_resids(tlmaps2, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

plot_qq(tlmaps2, xval_inds, 'LARGEST_GIFT_OR_PAYMENT')$outsample

Still can’t seem to fix those residuals.

Comparison

Check the models on out-of-sample data. Full results in the appendix.

# Predict largest transaction, PG score indicators
tlm_final <- tlms[[1]] %>% update(data = mdat %>% filter(rownum %in% unlist(xval_inds)))
# Predict largest transaction, underlying factors
tlmap_final <- tlmaps2[[1]] %>% update(data = mdat %>% filter(rownum %in% unlist(xval_inds)))

plot_resids(list(tlmap_final, tlm_final), list(oos_inds, oos_inds), 'LARGEST_GIFT_OR_PAYMENT')$outsample +
  scale_x_continuous(breaks = seq(0, 10, by = 2)) +
  scale_color_discrete(labels = c('All predictors', 'PG indicators')) +
  labs(color = 'Model')

Honestly, neither model looks great.

plot_qq(list(tlmap_final, tlm_final), list(oos_inds, oos_inds), 'LARGEST_GIFT_OR_PAYMENT')$outsample +
  scale_y_continuous(breaks = seq(-10, 10, by = 2)) +
  scale_color_discrete(labels = c('All predictors', 'PG indicators')) +
  labs(color = 'Model')

At least the the all predictors residuals lie closer to a normal distribution.

t_mses_final <- rbind(
  calc_preds(list(tlm_final), list(oos_inds), 'LARGEST_GIFT_OR_PAYMENT')[[1]] %>%
    mutate(model = 'PG indicators')
  , calc_preds(list(tlmap_final), list(oos_inds), 'LARGEST_GIFT_OR_PAYMENT')[[1]] %>%
    mutate(model = 'All predictors')
) %>% mutate(
  model = factor(model)
  , sq.error = (truth - preds)^2
)
t_mses_final <- t_mses_final %>% left_join(
  t_mses_final %>% group_by(model) %>% summarise(mse = mean(sq.error))
  , by = c('model', 'model')
)
t_mses_final %>%
  ggplot(aes(x = sq.error, color = model)) +
  geom_density() +
  geom_vline(aes(xintercept = mean(sq.error)), linetype = 'dotted', alpha = .5) +
  geom_vline(aes(xintercept = mse, color = model), linetype = 'dashed') +
  xlim(c(0, 5)) +
  labs(
    x = 'squared error'
    , title = bquote('Squared error across models, means = ' ~
        .(t_mses_final %>% group_by(model) %>% summarise(mse = mean(mse)) %>% select(mse) %>%
            unlist() %>% round(3) %>% paste(collapse = ', ')))
  )

Also very close to the previous results. Again, all predictors is a better regression model.

Finally, consider the effect of age on largest gift.

tlmap_splines_dat <- data.frame(
  age = mdat %>% filter(rownum %in% unlist(xval_inds)) %>% select(NUMERIC_AGE) %>% unlist()
  , y = model.frame(tlmap_final)[, 1]
  , y.hat = update(tlmap_final, formula = . ~ ns(NUMERIC_AGE, df = 3)) %>% fitted()
  # Compute partial residuals
  , y.partial.resid = {tlmap_final %>% residuals(type = 'partial')}[, 'ns(NUMERIC_AGE, df = 3)']
) %>% mutate(
  # Regression on partial residuals
  y.hat.partial = lm(y.partial.resid ~ ns(age, 3)) %>% fitted()
)

tlmap_splines_dat %>%
  ggplot(aes(x = age, y = y)) +
  geom_point(alpha = .1, size = 1) +
  geom_hline(yintercept = 2, color = 'darkgray') +
  geom_line(aes(y = y.hat), color = 'red') +
  labs(title = 'Numeric age spline versus largest transaction', y = bquote(log[10] ~ 'largest transaction'))

tlmap_splines_dat %>%
  ggplot(aes(x = age, y = y.partial.resid)) +
  geom_point(alpha = .1, size = 1) +
  geom_hline(yintercept = 0, color = 'darkgray') +
  geom_line(aes(y = y.hat.partial), color = 'red') +
  labs(title = 'Numeric age spline versus partial residuals for largest transaction'
       , y = 'partial residuals')

I find this surprising. Unlike in the campaign model comparison, largest gift size appears to increase monotonically with age. However, controlling for the other variables, we again see peak giving occurs in the late 40s or early 50s, though the fitted line is nearly flat (confirming the lesser impact of age in this model compared to the previous one). Apparently there are very few large estate gifts compared to the number of older donors.

Takeaway. Today’s age is not particularly predictive of largest gift; in future models consider age at time of gift instead. This could also be used to predict next gift size.

Thoughts

Here are the coefficients for each model.

full_join(
  data.frame(var = coef(tlm_final) %>% names, pg.inds.model = coef(tlm_final))
  , data.frame(var = coef(tlmap_final) %>% names, all.predictors.model = coef(tlmap_final))
) %>% mutate(
  # Factor levels in order we want them to appear in below table
  var = factor(var, levels = c('(Intercept)', 'ACTIVE_PROPOSALS'
    , 'AGE', 'ns(NUMERIC_AGE, df = 3)1', 'ns(NUMERIC_AGE, df = 3)2', 'ns(NUMERIC_AGE, df = 3)3'
    , 'PRESIDENT_VISIT', 'PM_VISIT_LAST_2_YRS', 'VISITS_5PLUS', 'log10plus1(VISIT_COUNT)'
    , 'AF_25K_GIFT', 'GAVE_IN_LAST_3_YRS', 'sqrt(YEARS_OF_GIVING)'
    , 'ns(YEARS_OF_GIVING_LAST_3, df = 2)1', 'ns(YEARS_OF_GIVING_LAST_3, df = 2)2', 'MG_250K_PLUS'
    , 'TRUSTEE_OR_ADVISORY_BOARD'
    , 'Alumnus', 'EVER_PARENT', 'DEEP_ENGAGEMENT', 'CHICAGO_HOME', 'ns(SEASON_TICKET_YEARS, df = 1)'
    , 'AFFINITY_SCORE', 'MG_PR_MODEL_DESCBottom Tier', 'MG_PR_MODEL_DESCMiddle Tier', 'MG_PR_MODEL_DESCTop Tier'))
) %>% arrange(var)

• The intercept is smaller in the all predictors model.
• Controlling for the other predictors, the effect of age is fairly minor.
• Unsurprisingly, past major giving is the strongest predictor of…major gift behavior.
• Again, the affinity score and prioritization models work – though here, bottom and middle tier don’t appear to have given at different levels in the past (which is likely why they weren’t top tier).

All of the PG indicators apparently help estimate largest gift amount, above and beyond the information that other variables and modeled scores provide. Importantly, with the exception of alumni status, all the signs point in the same direction.

Final Comparison

Rebuild all models using the entire dataset. Full results are in the appendix.

final_models <- list(
  campaign.pg.score = clm_final %>% update(data = mdat %>% filter(rownum %nin% outliers))
  , largest.trans.pg.score = tlm_final %>% update(data = mdat %>% filter(rownum %nin% outliers))
  , campaign.all.preds = clmap_final %>% update(data = mdat %>% filter(rownum %nin% outliers))
  , largest.trans.all.preds = tlmap_final %>% update(data = mdat %>% filter(rownum %nin% outliers))
)

full_join(
  data.frame(var = coef(final_models[[1]]) %>% names, campaign.pg = coef(final_models[[1]]))
  , data.frame(var = coef(final_models[[2]]) %>% names, largest.pg = coef(final_models[[2]]))
) %>% full_join(
  data.frame(var = coef(final_models[[3]]) %>% names, campaign.all = coef(final_models[[3]]))
) %>% full_join(
  data.frame(var = coef(final_models[[4]]) %>% names, largest.all = coef(final_models[[4]]))
) %>% mutate_if(is.numeric, function(x) round(x, 3))

Conclusions

Survivorship bias for age? Age at time of largest gift Regression model conditional on making a gift Modeling future gift conditional on past data only

Appendix

Campaign cultivation results

Back

lapply(clms, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8239 -1.2279 -0.1471  0.9867  5.6858 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.34693    0.02810  47.941  < 2e-16 ***
ACTIVE_PROPOSALS           0.29446    0.04558   6.460 1.07e-10 ***
AGE                       -0.21472    0.02142 -10.022  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.59623    0.04824  12.361  < 2e-16 ***
VISITS_5PLUS               0.75279    0.03485  21.598  < 2e-16 ***
AF_25K_GIFT                0.85477    0.06019  14.200  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03758    0.02332  87.385  < 2e-16 ***
MG_250K_PLUS               1.12287    0.10212  10.995  < 2e-16 ***
PRESIDENT_VISIT            0.42916    0.08478   5.062 4.19e-07 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26704    0.04512   5.918 3.31e-09 ***
Alumnus                   -0.11905    0.02771  -4.296 1.74e-05 ***
DEEP_ENGAGEMENT            0.32417    0.02300  14.093  < 2e-16 ***
CHICAGO_HOME               0.10052    0.02231   4.505 6.66e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 18765 degrees of freedom
Multiple R-squared:  0.4662,    Adjusted R-squared:  0.4659 
F-statistic:  1366 on 12 and 18765 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0307 -1.2214 -0.1514  0.9819  5.4201 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.33066    0.02805  47.436  < 2e-16 ***
ACTIVE_PROPOSALS           0.30700    0.04577   6.708 2.03e-11 ***
AGE                       -0.21829    0.02139 -10.204  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.59216    0.04831  12.259  < 2e-16 ***
VISITS_5PLUS               0.75868    0.03481  21.793  < 2e-16 ***
AF_25K_GIFT                0.79731    0.06102  13.067  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03779    0.02328  87.533  < 2e-16 ***
MG_250K_PLUS               1.17113    0.09934  11.789  < 2e-16 ***
PRESIDENT_VISIT            0.45526    0.08439   5.395 6.94e-08 ***
TRUSTEE_OR_ADVISORY_BOARD  0.28420    0.04532   6.270 3.68e-10 ***
Alumnus                   -0.10930    0.02767  -3.950 7.84e-05 ***
DEEP_ENGAGEMENT            0.34208    0.02290  14.940  < 2e-16 ***
CHICAGO_HOME               0.08937    0.02228   4.012 6.05e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.34 on 18765 degrees of freedom
Multiple R-squared:  0.4696,    Adjusted R-squared:  0.4693 
F-statistic:  1385 on 12 and 18765 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8276 -1.2195 -0.1510  0.9866  5.7040 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.33938    0.02800  47.838  < 2e-16 ***
ACTIVE_PROPOSALS           0.29632    0.04583   6.465 1.04e-10 ***
AGE                       -0.22454    0.02146 -10.465  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.61762    0.04842  12.754  < 2e-16 ***
VISITS_5PLUS               0.72950    0.03477  20.982  < 2e-16 ***
AF_25K_GIFT                0.84794    0.06027  14.070  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.04916    0.02337  87.684  < 2e-16 ***
MG_250K_PLUS               1.08151    0.10098  10.710  < 2e-16 ***
PRESIDENT_VISIT            0.45119    0.08407   5.367 8.12e-08 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26498    0.04551   5.822 5.90e-09 ***
Alumnus                   -0.11991    0.02762  -4.342 1.42e-05 ***
DEEP_ENGAGEMENT            0.33789    0.02294  14.731  < 2e-16 ***
CHICAGO_HOME               0.09713    0.02236   4.344 1.41e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.343 on 18765 degrees of freedom
Multiple R-squared:  0.4676,    Adjusted R-squared:  0.4673 
F-statistic:  1374 on 12 and 18765 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8414 -1.2329 -0.1434  0.9886  5.6898 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.36002    0.02802  48.532  < 2e-16 ***
ACTIVE_PROPOSALS           0.28005    0.04595   6.095 1.12e-09 ***
AGE                       -0.22377    0.02143 -10.440  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.61631    0.04860  12.680  < 2e-16 ***
VISITS_5PLUS               0.74954    0.03475  21.568  < 2e-16 ***
AF_25K_GIFT                0.87792    0.06035  14.547  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03790    0.02332  87.375  < 2e-16 ***
MG_250K_PLUS               1.08349    0.10146  10.679  < 2e-16 ***
PRESIDENT_VISIT            0.46548    0.08481   5.488 4.11e-08 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26591    0.04563   5.828 5.70e-09 ***
Alumnus                   -0.12710    0.02764  -4.598 4.29e-06 ***
DEEP_ENGAGEMENT            0.31337    0.02292  13.674  < 2e-16 ***
CHICAGO_HOME               0.09791    0.02231   4.388 1.15e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 18765 degrees of freedom
Multiple R-squared:  0.4654,    Adjusted R-squared:  0.4651 
F-statistic:  1362 on 12 and 18765 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8844 -1.2314 -0.1522  0.9870  5.6900 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.35196    0.02817  47.984  < 2e-16 ***
ACTIVE_PROPOSALS           0.26092    0.04625   5.641 1.71e-08 ***
AGE                       -0.22244    0.02151 -10.339  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.64613    0.04867  13.276  < 2e-16 ***
VISITS_5PLUS               0.75115    0.03509  21.406  < 2e-16 ***
AF_25K_GIFT                0.83049    0.06063  13.699  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03688    0.02345  86.864  < 2e-16 ***
MG_250K_PLUS               1.08036    0.10156  10.638  < 2e-16 ***
PRESIDENT_VISIT            0.48657    0.08422   5.777 7.70e-09 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26540    0.04542   5.843 5.21e-09 ***
Alumnus                   -0.12056    0.02779  -4.339 1.44e-05 ***
DEEP_ENGAGEMENT            0.33543    0.02308  14.536  < 2e-16 ***
CHICAGO_HOME               0.09086    0.02238   4.060 4.94e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.347 on 18765 degrees of freedom
Multiple R-squared:  0.4634,    Adjusted R-squared:  0.4631 
F-statistic:  1351 on 12 and 18765 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8287 -1.2295 -0.1432  0.9848  5.7026 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.35238    0.02807  48.176  < 2e-16 ***
ACTIVE_PROPOSALS           0.28734    0.04601   6.245 4.34e-10 ***
AGE                       -0.23307    0.02143 -10.877  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.59596    0.04873  12.230  < 2e-16 ***
VISITS_5PLUS               0.75457    0.03483  21.665  < 2e-16 ***
AF_25K_GIFT                0.84636    0.06145  13.774  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.04433    0.02338  87.450  < 2e-16 ***
MG_250K_PLUS               1.16030    0.10296  11.269  < 2e-16 ***
PRESIDENT_VISIT            0.43727    0.08545   5.117 3.13e-07 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26291    0.04535   5.798 6.83e-09 ***
Alumnus                   -0.12289    0.02762  -4.449 8.70e-06 ***
DEEP_ENGAGEMENT            0.33319    0.02297  14.506  < 2e-16 ***
CHICAGO_HOME               0.10104    0.02229   4.534 5.83e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 18765 degrees of freedom
Multiple R-squared:  0.4645,    Adjusted R-squared:  0.4642 
F-statistic:  1356 on 12 and 18765 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8764 -1.2307 -0.1563  0.9935  5.6954 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.33071    0.02823  47.131  < 2e-16 ***
ACTIVE_PROPOSALS           0.23338    0.04607   5.066 4.09e-07 ***
AGE                       -0.22714    0.02149 -10.571  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.63896    0.04846  13.185  < 2e-16 ***
VISITS_5PLUS               0.75737    0.03482  21.754  < 2e-16 ***
AF_25K_GIFT                0.84555    0.06045  13.987  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03324    0.02344  86.744  < 2e-16 ***
MG_250K_PLUS               1.13352    0.10301  11.004  < 2e-16 ***
PRESIDENT_VISIT            0.46217    0.08479   5.451 5.07e-08 ***
TRUSTEE_OR_ADVISORY_BOARD  0.27520    0.04521   6.087 1.17e-09 ***
Alumnus                   -0.09998    0.02778  -3.599  0.00032 ***
DEEP_ENGAGEMENT            0.34479    0.02311  14.918  < 2e-16 ***
CHICAGO_HOME               0.10005    0.02235   4.476 7.65e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.346 on 18765 degrees of freedom
Multiple R-squared:  0.463, Adjusted R-squared:  0.4627 
F-statistic:  1348 on 12 and 18765 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8964 -1.2328 -0.1415  0.9853  5.6799 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.34196    0.02812  47.718  < 2e-16 ***
ACTIVE_PROPOSALS           0.30133    0.04590   6.566 5.32e-11 ***
AGE                       -0.21375    0.02145  -9.966  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.59726    0.04869  12.267  < 2e-16 ***
VISITS_5PLUS               0.74967    0.03486  21.507  < 2e-16 ***
AF_25K_GIFT                0.81077    0.06108  13.275  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03577    0.02337  87.095  < 2e-16 ***
MG_250K_PLUS               1.11914    0.10216  10.955  < 2e-16 ***
PRESIDENT_VISIT            0.49984    0.08546   5.849 5.03e-09 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26241    0.04594   5.712 1.13e-08 ***
Alumnus                   -0.10918    0.02772  -3.938 8.24e-05 ***
DEEP_ENGAGEMENT            0.32971    0.02299  14.342  < 2e-16 ***
CHICAGO_HOME               0.09713    0.02237   4.341 1.42e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.345 on 18765 degrees of freedom
Multiple R-squared:  0.4632,    Adjusted R-squared:  0.4628 
F-statistic:  1349 on 12 and 18765 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8367 -1.2275 -0.1522  0.9824  5.6898 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.34522    0.02808  47.910  < 2e-16 ***
ACTIVE_PROPOSALS           0.28844    0.04549   6.340 2.34e-10 ***
AGE                       -0.21832    0.02151 -10.152  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.61760    0.04808  12.845  < 2e-16 ***
VISITS_5PLUS               0.74641    0.03491  21.383  < 2e-16 ***
AF_25K_GIFT                0.79725    0.06070  13.134  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.04948    0.02343  87.466  < 2e-16 ***
MG_250K_PLUS               1.06994    0.10179  10.511  < 2e-16 ***
PRESIDENT_VISIT            0.44647    0.08442   5.288 1.25e-07 ***
TRUSTEE_OR_ADVISORY_BOARD  0.26236    0.04543   5.775 7.80e-09 ***
Alumnus                   -0.11769    0.02767  -4.253 2.12e-05 ***
DEEP_ENGAGEMENT            0.32273    0.02304  14.006  < 2e-16 ***
CHICAGO_HOME               0.09442    0.02235   4.224 2.41e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.346 on 18765 degrees of freedom
Multiple R-squared:  0.4659,    Adjusted R-squared:  0.4655 
F-statistic:  1364 on 12 and 18765 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8200 -1.2335 -0.1495  0.9899  5.6845 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.34226    0.02813  47.722  < 2e-16 ***
ACTIVE_PROPOSALS           0.28224    0.04581   6.161 7.37e-10 ***
AGE                       -0.21910    0.02144 -10.221  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.60690    0.04828  12.571  < 2e-16 ***
VISITS_5PLUS               0.74335    0.03466  21.445  < 2e-16 ***
AF_25K_GIFT                0.86533    0.06029  14.353  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.03961    0.02335  87.365  < 2e-16 ***
MG_250K_PLUS               1.06492    0.10174  10.467  < 2e-16 ***
PRESIDENT_VISIT            0.41305    0.08505   4.857 1.20e-06 ***
TRUSTEE_OR_ADVISORY_BOARD  0.29067    0.04543   6.398 1.62e-10 ***
Alumnus                   -0.10872    0.02775  -3.918 8.96e-05 ***
DEEP_ENGAGEMENT            0.31888    0.02300  13.867  < 2e-16 ***
CHICAGO_HOME               0.10224    0.02240   4.565 5.03e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.345 on 18761 degrees of freedom
Multiple R-squared:  0.465, Adjusted R-squared:  0.4646 
F-statistic:  1359 on 12 and 18761 DF,  p-value: < 2.2e-16

Campaign all predictors 1

Back

lapply(clmaps, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2895 -0.6263 -0.1741  0.4893  5.4347 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.341446   0.111751   3.055 0.002251 ** 
ACTIVE_PROPOSALS                    -0.064470   0.032990  -1.954 0.050685 .  
ns(NUMERIC_AGE, df = 5)1             0.356430   0.086139   4.138 3.52e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.234530   0.100786   2.327 0.019975 *  
ns(NUMERIC_AGE, df = 5)3            -0.367925   0.069120  -5.323 1.03e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.434616   0.212833   2.042 0.041161 *  
ns(NUMERIC_AGE, df = 5)5            -0.498620   0.113826  -4.381 1.19e-05 ***
PM_VISIT_LAST_2_YRS                  0.311222   0.035286   8.820  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274852   0.029943   9.179  < 2e-16 ***
AF_25K_GIFT                          0.541953   0.043379  12.493  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.264019   0.006774  38.974  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.902994   0.042330  68.580  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.547836   0.025472  21.508  < 2e-16 ***
MG_250K_PLUS                         0.839794   0.077224  10.875  < 2e-16 ***
PRESIDENT_VISIT                      0.121050   0.062245   1.945 0.051820 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.057299   0.032569  -1.759 0.078543 .  
Alumnus                             -0.596510   0.024199 -24.650  < 2e-16 ***
DOUBLE_ALUM                          0.011842   0.022958   0.516 0.605998    
EVER_PARENT                         -0.056325   0.021948  -2.566 0.010288 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.245987   0.061748  -3.984 6.81e-05 ***
CHICAGO_HOME                        -0.039806   0.016750  -2.376 0.017492 *  
QUAL_LEVELA1 $100M+                  1.220711   0.484940   2.517 0.011836 *  
QUAL_LEVELA2 $50M - 99.9M            1.434297   0.433305   3.310 0.000934 ***
QUAL_LEVELA3 $25M - $49.9M           0.417629   0.216237   1.931 0.053455 .  
QUAL_LEVELA4 $10M - $24.9M           0.262164   0.149461   1.754 0.079436 .  
QUAL_LEVELA5 $5M - $9.9M             0.462304   0.113449   4.075 4.62e-05 ***
QUAL_LEVELA6 $2M - $4.9M             0.388985   0.102558   3.793 0.000149 ***
QUAL_LEVELA7 $1M - $1.9M             0.286411   0.081213   3.527 0.000422 ***
QUAL_LEVELB  $500K - $999K           0.065305   0.073322   0.891 0.373122    
QUAL_LEVELC  $250K - $499K          -0.001726   0.069408  -0.025 0.980166    
QUAL_LEVELD  $100K - $249K          -0.068684   0.068436  -1.004 0.315568    
QUAL_LEVELE  $50K - $99K            -0.173363   0.070001  -2.477 0.013273 *  
QUAL_LEVELF  $25K - $49K             0.005171   0.070218   0.074 0.941294    
QUAL_LEVELG  $10K - $24K            -0.159384   0.069455  -2.295 0.021756 *  
QUAL_LEVELH  Under $10K             -0.281272   0.223546  -1.258 0.208324    
QUAL_LEVELJ  Future Prospect        -1.277631   0.679280  -1.881 0.060006 .  
AFFINITY_SCORE                       0.169685   0.007186  23.612  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116074   0.022853  -5.079 3.83e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.242416   0.028692   8.449  < 2e-16 ***
MG_PR_MODEL_DESCTop Tier             0.573753   0.029048  19.752  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9551 on 18738 degrees of freedom
Multiple R-squared:  0.7308,    Adjusted R-squared:  0.7302 
F-statistic:  1304 on 39 and 18738 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2897 -0.6220 -0.1744  0.4872  5.6408 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.391491   0.110456   3.544 0.000395 ***
ACTIVE_PROPOSALS                    -0.063937   0.033186  -1.927 0.054044 .  
ns(NUMERIC_AGE, df = 5)1             0.328203   0.085189   3.853 0.000117 ***
ns(NUMERIC_AGE, df = 5)2             0.215162   0.099803   2.156 0.031107 *  
ns(NUMERIC_AGE, df = 5)3            -0.387400   0.067545  -5.735 9.88e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.416154   0.209315   1.988 0.046807 *  
ns(NUMERIC_AGE, df = 5)5            -0.447092   0.103769  -4.309 1.65e-05 ***
PM_VISIT_LAST_2_YRS                  0.305023   0.035400   8.616  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.273453   0.030004   9.114  < 2e-16 ***
AF_25K_GIFT                          0.505943   0.044017  11.494  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262934   0.006762  38.885  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.889784   0.042305  68.308  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.545837   0.025507  21.399  < 2e-16 ***
MG_250K_PLUS                         0.846511   0.075645  11.191  < 2e-16 ***
PRESIDENT_VISIT                      0.113156   0.062219   1.819 0.068979 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.054670   0.032815  -1.666 0.095727 .  
Alumnus                             -0.585837   0.024183 -24.225  < 2e-16 ***
DOUBLE_ALUM                          0.014434   0.022814   0.633 0.526955    
EVER_PARENT                         -0.047859   0.021898  -2.186 0.028862 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.232965   0.062273  -3.741 0.000184 ***
CHICAGO_HOME                        -0.040877   0.016729  -2.443 0.014556 *  
QUAL_LEVELA1 $100M+                  0.726674   0.433751   1.675 0.093887 .  
QUAL_LEVELA2 $50M - 99.9M            1.122095   0.396099   2.833 0.004618 ** 
QUAL_LEVELA3 $25M - $49.9M           0.580742   0.216842   2.678 0.007409 ** 
QUAL_LEVELA4 $10M - $24.9M           0.367308   0.154011   2.385 0.017092 *  
QUAL_LEVELA5 $5M - $9.9M             0.532593   0.111105   4.794 1.65e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.357393   0.102205   3.497 0.000472 ***
QUAL_LEVELA7 $1M - $1.9M             0.271406   0.080993   3.351 0.000807 ***
QUAL_LEVELB  $500K - $999K           0.038988   0.072917   0.535 0.592873    
QUAL_LEVELC  $250K - $499K          -0.033952   0.069026  -0.492 0.622817    
QUAL_LEVELD  $100K - $249K          -0.102750   0.068018  -1.511 0.130900    
QUAL_LEVELE  $50K - $99K            -0.206580   0.069616  -2.967 0.003007 ** 
QUAL_LEVELF  $25K - $49K            -0.016057   0.069841  -0.230 0.818167    
QUAL_LEVELG  $10K - $24K            -0.174895   0.069020  -2.534 0.011286 *  
QUAL_LEVELH  Under $10K             -0.280209   0.234367  -1.196 0.231868    
QUAL_LEVELJ  Future Prospect        -1.308141   0.678928  -1.927 0.054023 .  
AFFINITY_SCORE                       0.169215   0.007183  23.557  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.121312   0.022845  -5.310 1.11e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.238070   0.028686   8.299  < 2e-16 ***
MG_PR_MODEL_DESCTop Tier             0.582833   0.029210  19.953  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9546 on 18738 degrees of freedom
Multiple R-squared:  0.7312,    Adjusted R-squared:  0.7307 
F-statistic:  1307 on 39 and 18738 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2725 -0.6257 -0.1754  0.4919  5.7364 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.404583   0.112542   3.595 0.000325 ***
ACTIVE_PROPOSALS                    -0.071400   0.033262  -2.147 0.031837 *  
ns(NUMERIC_AGE, df = 5)1             0.285568   0.087031   3.281 0.001035 ** 
ns(NUMERIC_AGE, df = 5)2             0.151597   0.101923   1.487 0.136933    
ns(NUMERIC_AGE, df = 5)3            -0.441464   0.069324  -6.368 1.96e-10 ***
ns(NUMERIC_AGE, df = 5)4             0.295610   0.215133   1.374 0.169434    
ns(NUMERIC_AGE, df = 5)5            -0.458674   0.113424  -4.044 5.28e-05 ***
PM_VISIT_LAST_2_YRS                  0.325636   0.035581   9.152  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263200   0.029977   8.780  < 2e-16 ***
AF_25K_GIFT                          0.536419   0.043587  12.307  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.266381   0.006785  39.260  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.895646   0.042392  68.307  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.553333   0.025609  21.607  < 2e-16 ***
MG_250K_PLUS                         0.783007   0.076835  10.191  < 2e-16 ***
PRESIDENT_VISIT                      0.113368   0.061956   1.830 0.067294 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.056395   0.032916  -1.713 0.086672 .  
Alumnus                             -0.593741   0.024216 -24.518  < 2e-16 ***
DOUBLE_ALUM                          0.016607   0.022898   0.725 0.468308    
EVER_PARENT                         -0.047143   0.021997  -2.143 0.032114 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.252930   0.061219  -4.132 3.62e-05 ***
CHICAGO_HOME                        -0.040574   0.016835  -2.410 0.015959 *  
QUAL_LEVELA1 $100M+                  0.768902   0.435343   1.766 0.077379 .  
QUAL_LEVELA2 $50M - 99.9M            1.216988   0.484949   2.510 0.012098 *  
QUAL_LEVELA3 $25M - $49.9M           0.661902   0.236010   2.805 0.005044 ** 
QUAL_LEVELA4 $10M - $24.9M           0.322639   0.150113   2.149 0.031623 *  
QUAL_LEVELA5 $5M - $9.9M             0.504334   0.111437   4.526 6.06e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.424231   0.101644   4.174 3.01e-05 ***
QUAL_LEVELA7 $1M - $1.9M             0.307168   0.081771   3.756 0.000173 ***
QUAL_LEVELB  $500K - $999K           0.055312   0.073250   0.755 0.450189    
QUAL_LEVELC  $250K - $499K           0.001848   0.069363   0.027 0.978740    
QUAL_LEVELD  $100K - $249K          -0.065486   0.068360  -0.958 0.338096    
QUAL_LEVELE  $50K - $99K            -0.174596   0.069988  -2.495 0.012616 *  
QUAL_LEVELF  $25K - $49K             0.008160   0.070161   0.116 0.907415    
QUAL_LEVELG  $10K - $24K            -0.142123   0.069390  -2.048 0.040557 *  
QUAL_LEVELH  Under $10K             -0.262234   0.224164  -1.170 0.242084    
QUAL_LEVELJ  Future Prospect        -1.282014   0.681380  -1.881 0.059920 .  
AFFINITY_SCORE                       0.170403   0.007205  23.652  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110027   0.022910  -4.803 1.58e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.233596   0.028819   8.105 5.57e-16 ***
MG_PR_MODEL_DESCTop Tier             0.573006   0.029232  19.602  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9581 on 18738 degrees of freedom
Multiple R-squared:  0.7297,    Adjusted R-squared:  0.7291 
F-statistic:  1297 on 39 and 18738 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2709 -0.6226 -0.1777  0.4889  5.6790 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.364453   0.112016   3.254 0.001142 ** 
ACTIVE_PROPOSALS                    -0.068631   0.033344  -2.058 0.039577 *  
ns(NUMERIC_AGE, df = 5)1             0.342206   0.085931   3.982 6.85e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.229831   0.100531   2.286 0.022255 *  
ns(NUMERIC_AGE, df = 5)3            -0.425362   0.069194  -6.147 8.04e-10 ***
ns(NUMERIC_AGE, df = 5)4             0.476965   0.212390   2.246 0.024735 *  
ns(NUMERIC_AGE, df = 5)5            -0.375766   0.114094  -3.293 0.000991 ***
PM_VISIT_LAST_2_YRS                  0.316427   0.035724   8.857  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.285694   0.030000   9.523  < 2e-16 ***
AF_25K_GIFT                          0.553407   0.043627  12.685  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262587   0.006797  38.633  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.903585   0.042350  68.561  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.566545   0.025508  22.210  < 2e-16 ***
MG_250K_PLUS                         0.782409   0.076957  10.167  < 2e-16 ***
PRESIDENT_VISIT                      0.123001   0.062711   1.961 0.049848 *  
TRUSTEE_OR_ADVISORY_BOARD           -0.057048   0.033046  -1.726 0.084308 .  
Alumnus                             -0.596085   0.024245 -24.586  < 2e-16 ***
DOUBLE_ALUM                          0.004613   0.022957   0.201 0.840760    
EVER_PARENT                         -0.063259   0.021905  -2.888 0.003883 ** 
ns(SEASON_TICKET_YEARS, df = 1)     -0.252328   0.061494  -4.103 4.09e-05 ***
CHICAGO_HOME                        -0.038783   0.016822  -2.306 0.021149 *  
QUAL_LEVELA1 $100M+                  0.741595   0.435734   1.702 0.088782 .  
QUAL_LEVELA2 $50M - 99.9M            1.155604   0.397917   2.904 0.003687 ** 
QUAL_LEVELA3 $25M - $49.9M           0.550423   0.217925   2.526 0.011553 *  
QUAL_LEVELA4 $10M - $24.9M           0.395049   0.147932   2.670 0.007581 ** 
QUAL_LEVELA5 $5M - $9.9M             0.509263   0.111516   4.567 4.99e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.448581   0.104346   4.299 1.72e-05 ***
QUAL_LEVELA7 $1M - $1.9M             0.276262   0.081841   3.376 0.000738 ***
QUAL_LEVELB  $500K - $999K           0.043400   0.073677   0.589 0.555826    
QUAL_LEVELC  $250K - $499K          -0.002000   0.069700  -0.029 0.977108    
QUAL_LEVELD  $100K - $249K          -0.065492   0.068717  -0.953 0.340565    
QUAL_LEVELE  $50K - $99K            -0.175510   0.070339  -2.495 0.012597 *  
QUAL_LEVELF  $25K - $49K            -0.002979   0.070550  -0.042 0.966316    
QUAL_LEVELG  $10K - $24K            -0.155404   0.069806  -2.226 0.026012 *  
QUAL_LEVELH  Under $10K             -0.292918   0.229810  -1.275 0.202465    
QUAL_LEVELJ  Future Prospect        -1.604887   0.961722  -1.669 0.095181 .  
AFFINITY_SCORE                       0.168030   0.007220  23.272  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.120494   0.022974  -5.245 1.58e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.220163   0.028895   7.619 2.67e-14 ***
MG_PR_MODEL_DESCTop Tier             0.555617   0.029262  18.988  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9588 on 18738 degrees of freedom
Multiple R-squared:  0.7282,    Adjusted R-squared:  0.7276 
F-statistic:  1287 on 39 and 18738 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2347 -0.6301 -0.1769  0.4954  5.6709 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.385481   0.112055   3.440 0.000583 ***
ACTIVE_PROPOSALS                    -0.091010   0.033559  -2.712 0.006695 ** 
ns(NUMERIC_AGE, df = 5)1             0.315357   0.086665   3.639 0.000275 ***
ns(NUMERIC_AGE, df = 5)2             0.186084   0.101497   1.833 0.066760 .  
ns(NUMERIC_AGE, df = 5)3            -0.405596   0.069642  -5.824 5.84e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.369155   0.214564   1.720 0.085359 .  
ns(NUMERIC_AGE, df = 5)5            -0.420228   0.115698  -3.632 0.000282 ***
PM_VISIT_LAST_2_YRS                  0.351087   0.035817   9.802  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276443   0.030245   9.140  < 2e-16 ***
AF_25K_GIFT                          0.532387   0.043852  12.141  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261329   0.006798  38.443  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.896785   0.042546  68.087  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.547849   0.025684  21.330  < 2e-16 ***
MG_250K_PLUS                         0.800497   0.077118  10.380  < 2e-16 ***
PRESIDENT_VISIT                      0.125092   0.062108   2.014 0.044012 *  
TRUSTEE_OR_ADVISORY_BOARD           -0.065094   0.032902  -1.978 0.047893 *  
Alumnus                             -0.597361   0.024342 -24.541  < 2e-16 ***
DOUBLE_ALUM                          0.016604   0.023189   0.716 0.473982    
EVER_PARENT                         -0.057919   0.022132  -2.617 0.008879 ** 
ns(SEASON_TICKET_YEARS, df = 1)     -0.260390   0.062446  -4.170 3.06e-05 ***
CHICAGO_HOME                        -0.047823   0.016846  -2.839 0.004533 ** 
QUAL_LEVELA1 $100M+                  0.761863   0.437063   1.743 0.081325 .  
QUAL_LEVELA2 $50M - 99.9M            1.170506   0.399146   2.933 0.003366 ** 
QUAL_LEVELA3 $25M - $49.9M           0.658111   0.232021   2.836 0.004567 ** 
QUAL_LEVELA4 $10M - $24.9M           0.354742   0.149205   2.378 0.017438 *  
QUAL_LEVELA5 $5M - $9.9M             0.529395   0.111983   4.727 2.29e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.349239   0.101721   3.433 0.000598 ***
QUAL_LEVELA7 $1M - $1.9M             0.322173   0.082255   3.917 9.01e-05 ***
QUAL_LEVELB  $500K - $999K           0.066680   0.074003   0.901 0.367577    
QUAL_LEVELC  $250K - $499K           0.010894   0.070094   0.155 0.876488    
QUAL_LEVELD  $100K - $249K          -0.057196   0.069098  -0.828 0.407818    
QUAL_LEVELE  $50K - $99K            -0.167359   0.070684  -2.368 0.017908 *  
QUAL_LEVELF  $25K - $49K             0.018070   0.070957   0.255 0.798988    
QUAL_LEVELG  $10K - $24K            -0.150198   0.070064  -2.144 0.032067 *  
QUAL_LEVELH  Under $10K             -0.278074   0.230630  -1.206 0.227942    
QUAL_LEVELJ  Future Prospect        -1.281727   0.684037  -1.874 0.060978 .  
AFFINITY_SCORE                       0.170917   0.007221  23.669  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.115144   0.023018  -5.002 5.71e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.248133   0.028866   8.596  < 2e-16 ***
MG_PR_MODEL_DESCTop Tier             0.573315   0.029260  19.594  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9617 on 18738 degrees of freedom
Multiple R-squared:  0.727, Adjusted R-squared:  0.7264 
F-statistic:  1279 on 39 and 18738 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2449 -0.6239 -0.1764  0.4922  5.6765 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.352804   0.111907   3.153  0.00162 ** 
ACTIVE_PROPOSALS                    -0.068250   0.033304  -2.049  0.04044 *  
ns(NUMERIC_AGE, df = 5)1             0.373898   0.085409   4.378 1.21e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.214894   0.100002   2.149  0.03166 *  
ns(NUMERIC_AGE, df = 5)3            -0.388711   0.069060  -5.629 1.84e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.440838   0.211422   2.085  0.03707 *  
ns(NUMERIC_AGE, df = 5)5            -0.455911   0.114509  -3.981 6.88e-05 ***
PM_VISIT_LAST_2_YRS                  0.283859   0.035678   7.956 1.87e-15 ***
log10plus1(VISIT_COUNT)              0.303688   0.029989  10.127  < 2e-16 ***
AF_25K_GIFT                          0.544760   0.044284  12.301  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263631   0.006790  38.828  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.889769   0.042429  68.108  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.551496   0.025512  21.617  < 2e-16 ***
MG_250K_PLUS                         0.841226   0.077876  10.802  < 2e-16 ***
PRESIDENT_VISIT                      0.107264   0.062923   1.705  0.08827 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.070653   0.032772  -2.156  0.03111 *  
Alumnus                             -0.600663   0.024199 -24.822  < 2e-16 ***
DOUBLE_ALUM                          0.004623   0.022959   0.201  0.84044    
EVER_PARENT                         -0.054865   0.021972  -2.497  0.01253 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.247120   0.061505  -4.018 5.90e-05 ***
CHICAGO_HOME                        -0.034621   0.016758  -2.066  0.03885 *  
QUAL_LEVELA1 $100M+                  0.708448   0.484251   1.463  0.14349    
QUAL_LEVELA2 $50M - 99.9M            1.159842   0.397173   2.920  0.00350 ** 
QUAL_LEVELA3 $25M - $49.9M           0.559074   0.213457   2.619  0.00882 ** 
QUAL_LEVELA4 $10M - $24.9M           0.371337   0.152003   2.443  0.01458 *  
QUAL_LEVELA5 $5M - $9.9M             0.553517   0.113532   4.875 1.09e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.330123   0.104017   3.174  0.00151 ** 
QUAL_LEVELA7 $1M - $1.9M             0.327043   0.082665   3.956 7.64e-05 ***
QUAL_LEVELB  $500K - $999K           0.057462   0.074712   0.769  0.44183    
QUAL_LEVELC  $250K - $499K           0.007468   0.070772   0.106  0.91596    
QUAL_LEVELD  $100K - $249K          -0.050540   0.069833  -0.724  0.46924    
QUAL_LEVELE  $50K - $99K            -0.164136   0.071409  -2.299  0.02154 *  
QUAL_LEVELF  $25K - $49K             0.011728   0.071635   0.164  0.86996    
QUAL_LEVELG  $10K - $24K            -0.140502   0.070838  -1.983  0.04733 *  
QUAL_LEVELH  Under $10K             -0.531455   0.235345  -2.258  0.02394 *  
QUAL_LEVELJ  Future Prospect        -1.264960   0.680433  -1.859  0.06304 .  
AFFINITY_SCORE                       0.170109   0.007208  23.601  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131847   0.022948  -5.745 9.31e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.217021   0.028800   7.535 5.09e-14 ***
MG_PR_MODEL_DESCTop Tier             0.543232   0.029141  18.641  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9565 on 18738 degrees of freedom
Multiple R-squared:  0.7291,    Adjusted R-squared:  0.7286 
F-statistic:  1293 on 39 and 18738 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2851 -0.6251 -0.1747  0.4941  5.6212 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.346854   0.110443   3.141 0.001689 ** 
ACTIVE_PROPOSALS                    -0.105155   0.033343  -3.154 0.001614 ** 
ns(NUMERIC_AGE, df = 5)1             0.320110   0.085101   3.762 0.000169 ***
ns(NUMERIC_AGE, df = 5)2             0.200040   0.099742   2.006 0.044917 *  
ns(NUMERIC_AGE, df = 5)3            -0.464293   0.069248  -6.705 2.07e-11 ***
ns(NUMERIC_AGE, df = 5)4             0.427210   0.210857   2.026 0.042773 *  
ns(NUMERIC_AGE, df = 5)5            -0.364465   0.115968  -3.143 0.001676 ** 
PM_VISIT_LAST_2_YRS                  0.329574   0.035547   9.271  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301811   0.030020  10.054  < 2e-16 ***
AF_25K_GIFT                          0.552387   0.043573  12.677  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.266517   0.006781  39.305  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.878919   0.042437  67.839  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557458   0.025566  21.804  < 2e-16 ***
MG_250K_PLUS                         0.796596   0.077577  10.268  < 2e-16 ***
PRESIDENT_VISIT                      0.102970   0.062228   1.655 0.097997 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.051510   0.032666  -1.577 0.114844    
Alumnus                             -0.589388   0.024322 -24.233  < 2e-16 ***
DOUBLE_ALUM                          0.025664   0.023129   1.110 0.267177    
EVER_PARENT                         -0.055094   0.022095  -2.493 0.012659 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.237219   0.062710  -3.783 0.000156 ***
CHICAGO_HOME                        -0.036867   0.016776  -2.198 0.027986 *  
QUAL_LEVELA1 $100M+                  0.792449   0.435250   1.821 0.068672 .  
QUAL_LEVELA2 $50M - 99.9M            0.920865   0.484359   1.901 0.057291 .  
QUAL_LEVELA3 $25M - $49.9M           0.586341   0.225934   2.595 0.009461 ** 
QUAL_LEVELA4 $10M - $24.9M           0.372010   0.150190   2.477 0.013261 *  
QUAL_LEVELA5 $5M - $9.9M             0.583059   0.110893   5.258 1.47e-07 ***
QUAL_LEVELA6 $2M - $4.9M             0.356838   0.102873   3.469 0.000524 ***
QUAL_LEVELA7 $1M - $1.9M             0.322053   0.081231   3.965 7.38e-05 ***
QUAL_LEVELB  $500K - $999K           0.069831   0.073386   0.952 0.341330    
QUAL_LEVELC  $250K - $499K           0.016917   0.069385   0.244 0.807375    
QUAL_LEVELD  $100K - $249K          -0.051149   0.068407  -0.748 0.454635    
QUAL_LEVELE  $50K - $99K            -0.156419   0.070005  -2.234 0.025468 *  
QUAL_LEVELF  $25K - $49K             0.015232   0.070271   0.217 0.828401    
QUAL_LEVELG  $10K - $24K            -0.132805   0.069396  -1.914 0.055670 .  
QUAL_LEVELH  Under $10K             -0.203682   0.241516  -0.843 0.399045    
QUAL_LEVELJ  Future Prospect        -0.944384   0.961149  -0.983 0.325838    
AFFINITY_SCORE                       0.168162   0.007203  23.346  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.107480   0.022946  -4.684 2.83e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.238380   0.028842   8.265  < 2e-16 ***
MG_PR_MODEL_DESCTop Tier             0.557588   0.029195  19.099  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9578 on 18738 degrees of freedom
Multiple R-squared:  0.7284,    Adjusted R-squared:  0.7279 
F-statistic:  1289 on 39 and 18738 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2807 -0.6268 -0.1746  0.4972  5.6138 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.382629   0.113022   3.385 0.000712 ***
ACTIVE_PROPOSALS                    -0.063374   0.033423  -1.896 0.057960 .  
ns(NUMERIC_AGE, df = 5)1             0.377380   0.086519   4.362 1.30e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.274991   0.101330   2.714 0.006658 ** 
ns(NUMERIC_AGE, df = 5)3            -0.403471   0.069412  -5.813 6.25e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.536897   0.213888   2.510 0.012075 *  
ns(NUMERIC_AGE, df = 5)5            -0.361208   0.113673  -3.178 0.001487 ** 
PM_VISIT_LAST_2_YRS                  0.314056   0.035897   8.749  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.286774   0.030229   9.487  < 2e-16 ***
AF_25K_GIFT                          0.508165   0.044300  11.471  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261251   0.006836  38.218  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.876721   0.042474  67.729  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.544274   0.025720  21.162  < 2e-16 ***
MG_250K_PLUS                         0.818358   0.077474  10.563  < 2e-16 ***
PRESIDENT_VISIT                      0.105752   0.063399   1.668 0.095328 .  
TRUSTEE_OR_ADVISORY_BOARD           -0.067086   0.033334  -2.013 0.044176 *  
Alumnus                             -0.596046   0.024415 -24.413  < 2e-16 ***
DOUBLE_ALUM                          0.027571   0.023032   1.197 0.231309    
EVER_PARENT                         -0.064839   0.022138  -2.929 0.003406 ** 
ns(SEASON_TICKET_YEARS, df = 1)     -0.233376   0.062074  -3.760 0.000171 ***
CHICAGO_HOME                        -0.048085   0.016896  -2.846 0.004432 ** 
QUAL_LEVELA1 $100M+                  0.209403   0.486964   0.430 0.667188    
QUAL_LEVELA2 $50M - 99.9M            1.092914   0.399484   2.736 0.006228 ** 
QUAL_LEVELA3 $25M - $49.9M           0.559822   0.237247   2.360 0.018302 *  
QUAL_LEVELA4 $10M - $24.9M           0.334559   0.150036   2.230 0.025769 *  
QUAL_LEVELA5 $5M - $9.9M             0.495923   0.115704   4.286 1.83e-05 ***
QUAL_LEVELA6 $2M - $4.9M             0.346749   0.104109   3.331 0.000868 ***
QUAL_LEVELA7 $1M - $1.9M             0.226542   0.083347   2.718 0.006572 ** 
QUAL_LEVELB  $500K - $999K          -0.011676   0.075139  -0.155 0.876516    
QUAL_LEVELC  $250K - $499K          -0.063212   0.071198  -0.888 0.374640    
QUAL_LEVELD  $100K - $249K          -0.126086   0.070233  -1.795 0.072629 .  
QUAL_LEVELE  $50K - $99K            -0.242572   0.071821  -3.377 0.000733 ***
QUAL_LEVELF  $25K - $49K            -0.059704   0.072044  -0.829 0.407273    
QUAL_LEVELG  $10K - $24K            -0.210236   0.071221  -2.952 0.003162 ** 
QUAL_LEVELH  Under $10K             -0.197083   0.243128  -0.811 0.417596    
QUAL_LEVELJ  Future Prospect        -1.347452   0.684371  -1.969 0.048980 *  
AFFINITY_SCORE                       0.171930   0.007249  23.719  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110816   0.022981  -4.822 1.43e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.230762   0.028902   7.984 1.49e-15 ***
MG_PR_MODEL_DESCTop Tier             0.571219   0.029310  19.489  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.962 on 18738 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7251 
F-statistic:  1271 on 39 and 18738 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2872 -0.6215 -0.1758  0.4889  5.6850 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.346765   0.111376   3.113 0.001852 ** 
ACTIVE_PROPOSALS                    -0.063054   0.032974  -1.912 0.055861 .  
ns(NUMERIC_AGE, df = 5)1             0.366713   0.085852   4.271 1.95e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.250321   0.100573   2.489 0.012821 *  
ns(NUMERIC_AGE, df = 5)3            -0.379278   0.069240  -5.478 4.36e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.483587   0.212254   2.278 0.022717 *  
ns(NUMERIC_AGE, df = 5)5            -0.417211   0.114763  -3.635 0.000278 ***
PM_VISIT_LAST_2_YRS                  0.321170   0.035279   9.104  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274981   0.030052   9.150  < 2e-16 ***
AF_25K_GIFT                          0.499074   0.043796  11.396  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263008   0.006806  38.641  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.891665   0.042624  67.842  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.564097   0.025582  22.050  < 2e-16 ***
MG_250K_PLUS                         0.770537   0.076851  10.026  < 2e-16 ***
PRESIDENT_VISIT                      0.135153   0.062022   2.179 0.029337 *  
TRUSTEE_OR_ADVISORY_BOARD           -0.074513   0.032861  -2.268 0.023370 *  
Alumnus                             -0.595276   0.024279 -24.518  < 2e-16 ***
DOUBLE_ALUM                          0.012056   0.023024   0.524 0.600535    
EVER_PARENT                         -0.059858   0.022081  -2.711 0.006718 ** 
ns(SEASON_TICKET_YEARS, df = 1)     -0.286329   0.061743  -4.637 3.55e-06 ***
CHICAGO_HOME                        -0.042632   0.016811  -2.536 0.011225 *  
QUAL_LEVELA1 $100M+                  0.809436   0.559844   1.446 0.148243    
QUAL_LEVELA2 $50M - 99.9M            1.145850   0.398099   2.878 0.004003 ** 
QUAL_LEVELA3 $25M - $49.9M           0.509717   0.220884   2.308 0.021031 *  
QUAL_LEVELA4 $10M - $24.9M           0.287625   0.148298   1.940 0.052454 .  
QUAL_LEVELA5 $5M - $9.9M             0.507927   0.111796   4.543 5.57e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.384498   0.102731   3.743 0.000183 ***
QUAL_LEVELA7 $1M - $1.9M             0.278720   0.082053   3.397 0.000683 ***
QUAL_LEVELB  $500K - $999K           0.036884   0.073741   0.500 0.616947    
QUAL_LEVELC  $250K - $499K          -0.031306   0.069941  -0.448 0.654445    
QUAL_LEVELD  $100K - $249K          -0.080208   0.068895  -1.164 0.244359    
QUAL_LEVELE  $50K - $99K            -0.198185   0.070493  -2.811 0.004938 ** 
QUAL_LEVELF  $25K - $49K            -0.011455   0.070748  -0.162 0.871378    
QUAL_LEVELG  $10K - $24K            -0.173836   0.069913  -2.486 0.012911 *  
QUAL_LEVELH  Under $10K             -0.302310   0.219654  -1.376 0.168745    
QUAL_LEVELJ  Future Prospect        -1.301232   0.682217  -1.907 0.056489 .  
AFFINITY_SCORE                       0.171624   0.007207  23.813  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116209   0.023034  -5.045 4.57e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.236247   0.028984   8.151 3.84e-16 ***
MG_PR_MODEL_DESCTop Tier             0.569562   0.029192  19.511  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9592 on 18738 degrees of freedom
Multiple R-squared:  0.7293,    Adjusted R-squared:  0.7288 
F-statistic:  1295 on 39 and 18738 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2873 -0.6301 -0.1801  0.4971  5.7257 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.3122695  0.1110545   2.812 0.004931 ** 
ACTIVE_PROPOSALS                    -0.0736031  0.0333534  -2.207 0.027343 *  
ns(NUMERIC_AGE, df = 5)1             0.3488614  0.0855520   4.078 4.57e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.2494582  0.1001716   2.490 0.012772 *  
ns(NUMERIC_AGE, df = 5)3            -0.3885250  0.0691745  -5.617 1.98e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.4906668  0.2116286   2.319 0.020431 *  
ns(NUMERIC_AGE, df = 5)5            -0.4295347  0.1138515  -3.773 0.000162 ***
PM_VISIT_LAST_2_YRS                  0.3153022  0.0355707   8.864  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.2877440  0.0301351   9.548  < 2e-16 ***
AF_25K_GIFT                          0.5417772  0.0437565  12.382  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.2578015  0.0068164  37.821  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.8997166  0.0426106  68.052  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.5710922  0.0256487  22.266  < 2e-16 ***
MG_250K_PLUS                         0.7969775  0.0769718  10.354  < 2e-16 ***
PRESIDENT_VISIT                      0.0763249  0.0629863   1.212 0.225616    
TRUSTEE_OR_ADVISORY_BOARD           -0.0534279  0.0329842  -1.620 0.105292    
Alumnus                             -0.5849263  0.0243562 -24.015  < 2e-16 ***
DOUBLE_ALUM                         -0.0009347  0.0230807  -0.040 0.967699    
EVER_PARENT                         -0.0542565  0.0221380  -2.451 0.014262 *  
ns(SEASON_TICKET_YEARS, df = 1)     -0.2832474  0.0625200  -4.531 5.92e-06 ***
CHICAGO_HOME                        -0.0309689  0.0168936  -1.833 0.066792 .  
QUAL_LEVELA1 $100M+                  0.7871819  0.4372446   1.800 0.071826 .  
QUAL_LEVELA2 $50M - 99.9M            1.0705242  0.4359048   2.456 0.014064 *  
QUAL_LEVELA3 $25M - $49.9M           0.5868876  0.2214272   2.650 0.008045 ** 
QUAL_LEVELA4 $10M - $24.9M           0.3578239  0.1493322   2.396 0.016578 *  
QUAL_LEVELA5 $5M - $9.9M             0.4941051  0.1127425   4.383 1.18e-05 ***
QUAL_LEVELA6 $2M - $4.9M             0.3691003  0.1018378   3.624 0.000290 ***
QUAL_LEVELA7 $1M - $1.9M             0.2948647  0.0820841   3.592 0.000329 ***
QUAL_LEVELB  $500K - $999K           0.0633141  0.0736615   0.860 0.390060    
QUAL_LEVELC  $250K - $499K           0.0213256  0.0698541   0.305 0.760151    
QUAL_LEVELD  $100K - $249K          -0.0457723  0.0687890  -0.665 0.505802    
QUAL_LEVELE  $50K - $99K            -0.1586832  0.0704319  -2.253 0.024271 *  
QUAL_LEVELF  $25K - $49K             0.0178503  0.0706532   0.253 0.800544    
QUAL_LEVELG  $10K - $24K            -0.1331537  0.0698108  -1.907 0.056491 .  
QUAL_LEVELH  Under $10K             -0.2728308  0.2252079  -1.211 0.225734    
QUAL_LEVELJ  Future Prospect        -1.2680291  0.6843323  -1.853 0.063906 .  
AFFINITY_SCORE                       0.1727521  0.0072285  23.899  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.1110314  0.0231323  -4.800 1.60e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.2290186  0.0290201   7.892 3.14e-15 ***
MG_PR_MODEL_DESCTop Tier             0.5568048  0.0293331  18.982  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9622 on 18734 degrees of freedom
Multiple R-squared:  0.7265,    Adjusted R-squared:  0.7259 
F-statistic:  1276 on 39 and 18734 DF,  p-value: < 2.2e-16

Campaign all predictors 2

Back

lapply(clmaps2, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4291 -0.6237 -0.1842  0.4958  5.4709 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.255115   0.094156   2.709  0.00675 ** 
ns(NUMERIC_AGE, df = 5)1             0.386056   0.086031   4.487 7.25e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.250456   0.100715   2.487  0.01290 *  
ns(NUMERIC_AGE, df = 5)3            -0.347147   0.069289  -5.010 5.49e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.464897   0.213109   2.181  0.02916 *  
ns(NUMERIC_AGE, df = 5)5            -0.546338   0.114141  -4.787 1.71e-06 ***
PM_VISIT_LAST_2_YRS                  0.311628   0.028735  10.845  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274347   0.028878   9.500  < 2e-16 ***
AF_25K_GIFT                          0.615943   0.042566  14.470  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260803   0.006777  38.485  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929400   0.042294  69.262  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558071   0.025498  21.887  < 2e-16 ***
MG_250K_PLUS                         1.125296   0.071607  15.715  < 2e-16 ***
Alumnus                             -0.572103   0.021390 -26.746  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263700   0.061203  -4.309 1.65e-05 ***
AFFINITY_SCORE                       0.158247   0.006798  23.279  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117862   0.022128  -5.326 1.01e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.214713   0.028350   7.574 3.80e-14 ***
MG_PR_MODEL_DESCTop Tier             0.595287   0.028195  21.113  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9611 on 18759 degrees of freedom
Multiple R-squared:  0.7271,    Adjusted R-squared:  0.7268 
F-statistic:  2776 on 18 and 18759 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4727 -0.6214 -0.1825  0.4944  6.2743 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.283317   0.093030   3.045  0.00233 ** 
ns(NUMERIC_AGE, df = 5)1             0.359258   0.085039   4.225 2.40e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.230563   0.099705   2.312  0.02076 *  
ns(NUMERIC_AGE, df = 5)3            -0.369434   0.067707  -5.456 4.92e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.450814   0.209545   2.151  0.03146 *  
ns(NUMERIC_AGE, df = 5)5            -0.482863   0.104079  -4.639 3.52e-06 ***
PM_VISIT_LAST_2_YRS                  0.302190   0.028790  10.496  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279258   0.028960   9.643  < 2e-16 ***
AF_25K_GIFT                          0.588082   0.043198  13.614  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259590   0.006763  38.383  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913908   0.042293  68.898  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557706   0.025538  21.839  < 2e-16 ***
MG_250K_PLUS                         1.166991   0.070045  16.661  < 2e-16 ***
Alumnus                             -0.568739   0.021427 -26.543  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256878   0.061807  -4.156 3.25e-05 ***
AFFINITY_SCORE                       0.158592   0.006794  23.343  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123273   0.022106  -5.576 2.49e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208271   0.028348   7.347 2.11e-13 ***
MG_PR_MODEL_DESCTop Tier             0.600171   0.028343  21.175  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.961 on 18759 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.7271 
F-statistic:  2780 on 18 and 18759 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3972 -0.6247 -0.1861  0.4948  6.3062 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.331319   0.095104   3.484 0.000496 ***
ns(NUMERIC_AGE, df = 5)1             0.310424   0.086897   3.572 0.000355 ***
ns(NUMERIC_AGE, df = 5)2             0.161271   0.101850   1.583 0.113343    
ns(NUMERIC_AGE, df = 5)3            -0.422841   0.069459  -6.088 1.17e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.320024   0.215424   1.486 0.137415    
ns(NUMERIC_AGE, df = 5)5            -0.497399   0.113736  -4.373 1.23e-05 ***
PM_VISIT_LAST_2_YRS                  0.319925   0.028793  11.111  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263066   0.028893   9.105  < 2e-16 ***
AF_25K_GIFT                          0.613941   0.042757  14.359  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262874   0.006788  38.726  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.916592   0.042389  68.805  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563381   0.025631  21.980  < 2e-16 ***
MG_250K_PLUS                         1.089595   0.071116  15.321  < 2e-16 ***
Alumnus                             -0.574934   0.021439 -26.818  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275646   0.060635  -4.546 5.50e-06 ***
AFFINITY_SCORE                       0.160383   0.006823  23.506  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110457   0.022177  -4.981 6.39e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207385   0.028463   7.286 3.32e-13 ***
MG_PR_MODEL_DESCTop Tier             0.594950   0.028351  20.985  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.964 on 18759 degrees of freedom
Multiple R-squared:  0.726, Adjusted R-squared:  0.7257 
F-statistic:  2761 on 18 and 18759 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4015 -0.6252 -0.1848  0.4979  6.2642 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.274694   0.093894   2.926 0.003442 ** 
ns(NUMERIC_AGE, df = 5)1             0.375171   0.085767   4.374 1.22e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.250632   0.100418   2.496 0.012573 *  
ns(NUMERIC_AGE, df = 5)3            -0.405832   0.069359  -5.851 4.96e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.526632   0.212578   2.477 0.013245 *  
ns(NUMERIC_AGE, df = 5)5            -0.418507   0.114422  -3.658 0.000255 ***
PM_VISIT_LAST_2_YRS                  0.319594   0.028956  11.037  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287850   0.028926   9.951  < 2e-16 ***
AF_25K_GIFT                          0.625386   0.042895  14.579  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259398   0.006796  38.169  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.925863   0.042344  69.098  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.578451   0.025528  22.659  < 2e-16 ***
MG_250K_PLUS                         1.092788   0.071386  15.308  < 2e-16 ***
Alumnus                             -0.572718   0.021470 -26.675  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.269615   0.060948  -4.424 9.76e-06 ***
AFFINITY_SCORE                       0.155681   0.006829  22.797  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.124106   0.022225  -5.584 2.38e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.192985   0.028538   6.762 1.40e-11 ***
MG_PR_MODEL_DESCTop Tier             0.576692   0.028387  20.315  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9648 on 18759 degrees of freedom
Multiple R-squared:  0.7244,    Adjusted R-squared:  0.7242 
F-statistic:  2740 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1069 -0.6275 -0.1859  0.4998  6.2731 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.304096   0.094649   3.213  0.00132 ** 
ns(NUMERIC_AGE, df = 5)1             0.349770   0.086479   4.045 5.26e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.205294   0.101354   2.026  0.04283 *  
ns(NUMERIC_AGE, df = 5)3            -0.385610   0.069824  -5.523 3.38e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.411601   0.214714   1.917  0.05526 .  
ns(NUMERIC_AGE, df = 5)5            -0.467467   0.116063  -4.028 5.65e-05 ***
PM_VISIT_LAST_2_YRS                  0.339281   0.029088  11.664  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276994   0.029135   9.507  < 2e-16 ***
AF_25K_GIFT                          0.607389   0.043052  14.108  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258651   0.006802  38.026  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917820   0.042572  68.539  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.559188   0.025716  21.745  < 2e-16 ***
MG_250K_PLUS                         1.097125   0.071871  15.265  < 2e-16 ***
Alumnus                             -0.573888   0.021552 -26.628  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.284875   0.061937  -4.599 4.26e-06 ***
AFFINITY_SCORE                       0.158616   0.006845  23.171  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116487   0.022304  -5.223 1.78e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221006   0.028528   7.747 9.88e-15 ***
MG_PR_MODEL_DESCTop Tier             0.594491   0.028418  20.920  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18759 degrees of freedom
Multiple R-squared:  0.723, Adjusted R-squared:  0.7227 
F-statistic:  2720 on 18 and 18759 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4361 -0.6200 -0.1867  0.4945  6.2946 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.278270   0.093230   2.985  0.00284 ** 
ns(NUMERIC_AGE, df = 5)1             0.410210   0.085208   4.814 1.49e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.233923   0.099899   2.342  0.01921 *  
ns(NUMERIC_AGE, df = 5)3            -0.361744   0.069187  -5.229 1.73e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.484563   0.211617   2.290  0.02204 *  
ns(NUMERIC_AGE, df = 5)5            -0.497700   0.114815  -4.335 1.47e-05 ***
PM_VISIT_LAST_2_YRS                  0.283623   0.029129   9.737  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300906   0.028885  10.417  < 2e-16 ***
AF_25K_GIFT                          0.614608   0.043478  14.136  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260185   0.006788  38.331  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913073   0.042421  68.670  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562183   0.025537  22.014  < 2e-16 ***
MG_250K_PLUS                         1.139511   0.072230  15.776  < 2e-16 ***
Alumnus                             -0.581322   0.021355 -27.222  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.260444   0.060938  -4.274 1.93e-05 ***
AFFINITY_SCORE                       0.158698   0.006821  23.265  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.132689   0.022198  -5.978 2.31e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.191770   0.028444   6.742 1.61e-11 ***
MG_PR_MODEL_DESCTop Tier             0.565934   0.028285  20.008  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9624 on 18759 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7252 
F-statistic:  2754 on 18 and 18759 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3994 -0.6266 -0.1874  0.4958  6.2636 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.283086   0.093027   3.043 0.002345 ** 
ns(NUMERIC_AGE, df = 5)1             0.349749   0.084895   4.120 3.81e-05 ***
ns(NUMERIC_AGE, df = 5)2             0.214313   0.099606   2.152 0.031441 *  
ns(NUMERIC_AGE, df = 5)3            -0.438830   0.069369  -6.326 2.57e-10 ***
ns(NUMERIC_AGE, df = 5)4             0.456633   0.211028   2.164 0.030489 *  
ns(NUMERIC_AGE, df = 5)5            -0.411888   0.116315  -3.541 0.000399 ***
PM_VISIT_LAST_2_YRS                  0.305546   0.028936  10.560  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299356   0.028897  10.359  < 2e-16 ***
AF_25K_GIFT                          0.623798   0.042930  14.531  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263053   0.006784  38.776  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899286   0.042408  68.366  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567406   0.025584  22.179  < 2e-16 ***
MG_250K_PLUS                         1.092580   0.072594  15.051  < 2e-16 ***
Alumnus                             -0.570026   0.021460 -26.562  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259960   0.062182  -4.181 2.92e-05 ***
AFFINITY_SCORE                       0.157965   0.006824  23.149  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109675   0.022215  -4.937 8.01e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.212359   0.028477   7.457 9.23e-14 ***
MG_PR_MODEL_DESCTop Tier             0.578368   0.028332  20.414  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18759 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7245 
F-statistic:  2745 on 18 and 18759 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4493 -0.6252 -0.1804  0.4957  6.2555 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.242819   0.094508   2.569 0.010198 *  
ns(NUMERIC_AGE, df = 5)1             0.400320   0.086354   4.636 3.58e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.280377   0.101216   2.770 0.005610 ** 
ns(NUMERIC_AGE, df = 5)3            -0.386612   0.069558  -5.558 2.76e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.554027   0.214110   2.588 0.009673 ** 
ns(NUMERIC_AGE, df = 5)5            -0.417324   0.114021  -3.660 0.000253 ***
PM_VISIT_LAST_2_YRS                  0.318603   0.029185  10.917  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287104   0.029101   9.866  < 2e-16 ***
AF_25K_GIFT                          0.576878   0.043685  13.206  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258682   0.006836  37.844  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901273   0.042488  68.284  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555337   0.025751  21.565  < 2e-16 ***
MG_250K_PLUS                         1.127600   0.072302  15.596  < 2e-16 ***
Alumnus                             -0.568027   0.021564 -26.341  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.257464   0.061525  -4.185 2.87e-05 ***
AFFINITY_SCORE                       0.160057   0.006853  23.356  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117462   0.022260  -5.277 1.33e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200356   0.028555   7.016 2.35e-12 ***
MG_PR_MODEL_DESCTop Tier             0.585297   0.028422  20.593  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18759 degrees of freedom
Multiple R-squared:  0.7218,    Adjusted R-squared:  0.7216 
F-statistic:  2704 on 18 and 18759 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3846 -0.6212 -0.1874  0.4930  6.2869 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.240996   0.093787   2.570   0.0102 *  
ns(NUMERIC_AGE, df = 5)1             0.398169   0.085670   4.648 3.38e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.265551   0.100441   2.644   0.0082 ** 
ns(NUMERIC_AGE, df = 5)3            -0.358989   0.069381  -5.174 2.31e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.521546   0.212440   2.455   0.0141 *  
ns(NUMERIC_AGE, df = 5)5            -0.457178   0.115156  -3.970 7.21e-05 ***
PM_VISIT_LAST_2_YRS                  0.324927   0.028819  11.275  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276194   0.028930   9.547  < 2e-16 ***
AF_25K_GIFT                          0.569050   0.043065  13.214  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259652   0.006808  38.138  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.914343   0.042620  68.379  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.576876   0.025602  22.532  < 2e-16 ***
MG_250K_PLUS                         1.071882   0.071627  14.965  < 2e-16 ***
Alumnus                             -0.570608   0.021474 -26.572  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.303422   0.061201  -4.958 7.19e-07 ***
AFFINITY_SCORE                       0.159975   0.006819  23.460  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.119662   0.022296  -5.367 8.10e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208362   0.028645   7.274 3.63e-13 ***
MG_PR_MODEL_DESCTop Tier             0.588830   0.028309  20.800  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9655 on 18759 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7252 
F-statistic:  2754 on 18 and 18759 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3449 -0.6302 -0.1870  0.5008  6.2783 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.245968   0.093356   2.635  0.00843 ** 
ns(NUMERIC_AGE, df = 5)1             0.385387   0.085217   4.522 6.15e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.270300   0.099870   2.707  0.00681 ** 
ns(NUMERIC_AGE, df = 5)3            -0.364235   0.069246  -5.260 1.46e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.538473   0.211455   2.547  0.01089 *  
ns(NUMERIC_AGE, df = 5)5            -0.465707   0.114036  -4.084 4.45e-05 ***
PM_VISIT_LAST_2_YRS                  0.311610   0.028905  10.781  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.283431   0.029058   9.754  < 2e-16 ***
AF_25K_GIFT                          0.612449   0.042930  14.266  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.254815   0.006813  37.400  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.919716   0.042574  68.580  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582585   0.025656  22.708  < 2e-16 ***
MG_250K_PLUS                         1.062557   0.071953  14.767  < 2e-16 ***
Alumnus                             -0.567552   0.021520 -26.373  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.293029   0.061929  -4.732 2.24e-06 ***
AFFINITY_SCORE                       0.161414   0.006832  23.628  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.112613   0.022355  -5.037 4.76e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.203528   0.028624   7.111 1.20e-12 ***
MG_PR_MODEL_DESCTop Tier             0.577537   0.028417  20.323  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9675 on 18755 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7229 
F-statistic:  2721 on 18 and 18755 DF,  p-value: < 2.2e-16

Campaign all predictors splines

Back

lapply(clmaps3
  , function(x) {
    lapply(x, function(y) summary(y))
  }
)

[[1]]
[[1]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3996 -0.6295 -0.1754  0.4992  5.5008 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.011648   0.035936  28.151  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.350353   0.074412 -18.147  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.325019   0.028767  11.298  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.272621   0.028949   9.417  < 2e-16 ***
AF_25K_GIFT                          0.627879   0.042636  14.727  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259172   0.006755  38.369  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.951105   0.042358  69.670  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.566090   0.025556  22.151  < 2e-16 ***
MG_250K_PLUS                         1.112309   0.071750  15.502  < 2e-16 ***
Alumnus                             -0.562567   0.020878 -26.945  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256748   0.061342  -4.186 2.86e-05 ***
AFFINITY_SCORE                       0.151678   0.006755  22.453  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.097397   0.022043  -4.418 1.00e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.232656   0.028336   8.211 2.34e-16 ***
MG_PR_MODEL_DESCTop Tier             0.616852   0.028159  21.906  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9638 on 18763 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7253 
F-statistic:  3542 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4424 -0.6269 -0.1756  0.4982  6.3167 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.011224   0.035844  28.212  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.303478   0.072326 -18.022  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.313448   0.028825  10.874  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277762   0.029028   9.569  < 2e-16 ***
AF_25K_GIFT                          0.601112   0.043263  13.895  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.257642   0.006743  38.210  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.938189   0.042340  69.395  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.566231   0.025590  22.127  < 2e-16 ***
MG_250K_PLUS                         1.155986   0.070177  16.473  < 2e-16 ***
Alumnus                             -0.559664   0.020901 -26.777  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.253058   0.061955  -4.085 4.43e-05 ***
AFFINITY_SCORE                       0.151976   0.006751  22.510  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.102657   0.022011  -4.664 3.12e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.226995   0.028326   8.014 1.18e-15 ***
MG_PR_MODEL_DESCTop Tier             0.622891   0.028283  22.023  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18763 degrees of freedom
Multiple R-squared:  0.7258,    Adjusted R-squared:  0.7256 
F-statistic:  3547 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3651 -0.6291 -0.1780  0.4978  6.3608 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.014388   0.035945  28.220  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.376220   0.074320 -18.518  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.330153   0.028824  11.454  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.262787   0.028953   9.076  < 2e-16 ***
AF_25K_GIFT                          0.624961   0.042805  14.600  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260261   0.006763  38.483  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.940863   0.042426  69.317  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.571570   0.025680  22.257  < 2e-16 ***
MG_250K_PLUS                         1.078021   0.071230  15.134  < 2e-16 ***
Alumnus                             -0.565383   0.020899 -27.053  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.272391   0.060763  -4.483 7.41e-06 ***
AFFINITY_SCORE                       0.154440   0.006779  22.783  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.091209   0.022082  -4.131 3.63e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.224616   0.028440   7.898 2.99e-15 ***
MG_PR_MODEL_DESCTop Tier             0.615312   0.028292  21.748  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9665 on 18763 degrees of freedom
Multiple R-squared:  0.7245,    Adjusted R-squared:  0.7243 
F-statistic:  3525 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3687 -0.6289 -0.1794  0.5053  6.3332 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.025517   0.036033  28.460  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.341706   0.074517 -18.005  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.329548   0.029005  11.362  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287559   0.029000   9.916  < 2e-16 ***
AF_25K_GIFT                          0.637148   0.042965  14.829  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.256744   0.006775  37.898  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.951075   0.042396  69.608  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.585934   0.025590  22.897  < 2e-16 ***
MG_250K_PLUS                         1.080915   0.071526  15.112  < 2e-16 ***
Alumnus                             -0.561766   0.020942 -26.825  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.266128   0.061094  -4.356 1.33e-05 ***
AFFINITY_SCORE                       0.149421   0.006789  22.008  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.105363   0.022143  -4.758 1.97e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.210170   0.028516   7.370 1.77e-13 ***
MG_PR_MODEL_DESCTop Tier             0.597198   0.028339  21.073  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9676 on 18763 degrees of freedom
Multiple R-squared:  0.7228,    Adjusted R-squared:  0.7226 
F-statistic:  3494 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1738 -0.6321 -0.1803  0.5071  6.3228 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.011858   0.036129  28.007  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.327373   0.074862 -17.731  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.350895   0.029123  12.049  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.275742   0.029204   9.442  < 2e-16 ***
AF_25K_GIFT                          0.619291   0.043116  14.363  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.256291   0.006777  37.815  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.941430   0.042618  69.018  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567490   0.025768  22.023  < 2e-16 ***
MG_250K_PLUS                         1.085089   0.072006  15.069  < 2e-16 ***
Alumnus                             -0.564320   0.020998 -26.875  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.276863   0.062075  -4.460 8.24e-06 ***
AFFINITY_SCORE                       0.152335   0.006804  22.389  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.096627   0.022209  -4.351 1.36e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.239469   0.028503   8.402  < 2e-16 ***
MG_PR_MODEL_DESCTop Tier             0.615913   0.028366  21.713  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9708 on 18763 degrees of freedom
Multiple R-squared:  0.7215,    Adjusted R-squared:  0.7212 
F-statistic:  3471 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4019 -0.6267 -0.1800  0.5021  6.3318 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.03919    0.03595  28.905  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.36968    0.07440 -18.409  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.29636    0.02918  10.155  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.30111    0.02897  10.393  < 2e-16 ***
AF_25K_GIFT                          0.62749    0.04357  14.402  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.25738    0.00677  38.019  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.94250    0.04248  69.275  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.57019    0.02561  22.267  < 2e-16 ***
MG_250K_PLUS                         1.12562    0.07241  15.544  < 2e-16 ***
Alumnus                             -0.56813    0.02084 -27.266  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.25377    0.06111  -4.153 3.30e-05 ***
AFFINITY_SCORE                       0.15203    0.00678  22.422  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.10938    0.02210  -4.948 7.54e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.21216    0.02843   7.462 8.87e-14 ***
MG_PR_MODEL_DESCTop Tier             0.59039    0.02824  20.904  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9656 on 18763 degrees of freedom
Multiple R-squared:  0.7236,    Adjusted R-squared:  0.7234 
F-statistic:  3509 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3667 -0.6311 -0.1796  0.5034  6.3455 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.011044   0.036031  28.061  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.369740   0.074490 -18.388  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.316452   0.028983  10.919  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.298130   0.028976  10.289  < 2e-16 ***
AF_25K_GIFT                          0.635174   0.043014  14.767  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260128   0.006766  38.446  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.924981   0.042463  68.883  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.575521   0.025644  22.443  < 2e-16 ***
MG_250K_PLUS                         1.081976   0.072759  14.871  < 2e-16 ***
Alumnus                             -0.559264   0.020915 -26.740  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.254369   0.062341  -4.080 4.52e-05 ***
AFFINITY_SCORE                       0.151675   0.006784  22.359  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.090844   0.022137  -4.104 4.08e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.229319   0.028473   8.054 8.49e-16 ***
MG_PR_MODEL_DESCTop Tier             0.599729   0.028290  21.200  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9665 on 18763 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7229 
F-statistic:  3500 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4171 -0.6331 -0.1760  0.5027  6.3109 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.010263   0.036100  27.985  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.320875   0.074626 -17.700  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.329760   0.029233  11.281  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.285037   0.029177   9.769  < 2e-16 ***
AF_25K_GIFT                          0.592203   0.043761  13.533  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.256081   0.006812  37.590  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.927337   0.042545  68.805  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.564427   0.025811  21.868  < 2e-16 ***
MG_250K_PLUS                         1.119567   0.072456  15.452  < 2e-16 ***
Alumnus                             -0.559004   0.021015 -26.600  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.251986   0.061683  -4.085 4.42e-05 ***
AFFINITY_SCORE                       0.153385   0.006811  22.521  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.096114   0.022170  -4.335 1.46e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.219171   0.028544   7.678 1.69e-14 ***
MG_PR_MODEL_DESCTop Tier             0.608056   0.028378  21.427  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9711 on 18763 degrees of freedom
Multiple R-squared:  0.7201,    Adjusted R-squared:  0.7199 
F-statistic:  3448 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3526 -0.6266 -0.1798  0.4974  6.3268 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.995951   0.036160  27.543  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.310639   0.074769 -17.529  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.337107   0.028861  11.680  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274510   0.029006   9.464  < 2e-16 ***
AF_25K_GIFT                          0.582543   0.043143  13.503  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.257403   0.006790  37.909  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.940231   0.042673  68.901  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.586084   0.025660  22.841  < 2e-16 ***
MG_250K_PLUS                         1.061446   0.071791  14.785  < 2e-16 ***
Alumnus                             -0.560707   0.020934 -26.784  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.296665   0.061347  -4.836 1.34e-06 ***
AFFINITY_SCORE                       0.153127   0.006775  22.603  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.097670   0.022199  -4.400 1.09e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.228182   0.028627   7.971 1.66e-15 ***
MG_PR_MODEL_DESCTop Tier             0.611407   0.028263  21.632  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9683 on 18763 degrees of freedom
Multiple R-squared:  0.7238,    Adjusted R-squared:  0.7236 
F-statistic:  3512 on 14 and 18763 DF,  p-value: < 2.2e-16


[[1]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3157 -0.6334 -0.1816  0.5059  6.3183 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.000846   0.036132  27.700  < 2e-16 ***
ns(NUMERIC_AGE, df = s)             -1.308831   0.074566 -17.553  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.323402   0.028938  11.176  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.281649   0.029129   9.669  < 2e-16 ***
AF_25K_GIFT                          0.625439   0.043002  14.545  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.252613   0.006790  37.205  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.944353   0.042620  69.084  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.591298   0.025712  22.997  < 2e-16 ***
MG_250K_PLUS                         1.052868   0.072106  14.602  < 2e-16 ***
Alumnus                             -0.556932   0.020978 -26.548  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.289386   0.062083  -4.661 3.16e-06 ***
AFFINITY_SCORE                       0.154948   0.006792  22.812  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.093206   0.022268  -4.186 2.86e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.220819   0.028608   7.719 1.23e-14 ***
MG_PR_MODEL_DESCTop Tier             0.598102   0.028373  21.080  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9702 on 18759 degrees of freedom
Multiple R-squared:  0.7215,    Adjusted R-squared:  0.7213 
F-statistic:  3472 on 14 and 18759 DF,  p-value: < 2.2e-16



[[2]]
[[2]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4259 -0.6249 -0.1741  0.4978  5.4738 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.777184   0.054479  14.266  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.842164   0.094649  -8.898  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.071294   0.059850 -17.900  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.315957   0.028786  10.976  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276454   0.028932   9.555  < 2e-16 ***
AF_25K_GIFT                          0.617062   0.042642  14.471  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.256970   0.006760  38.013  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.946377   0.042331  69.604  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562856   0.025541  22.038  < 2e-16 ***
MG_250K_PLUS                         1.128694   0.071747  15.732  < 2e-16 ***
Alumnus                             -0.549836   0.020979 -26.209  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.267310   0.061318  -4.359 1.31e-05 ***
AFFINITY_SCORE                       0.156518   0.006803  23.009  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110929   0.022151  -5.008 5.56e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221203   0.028383   7.794 6.85e-15 ***
MG_PR_MODEL_DESCTop Tier             0.604322   0.028220  21.415  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.963 on 18762 degrees of freedom
Multiple R-squared:  0.726, Adjusted R-squared:  0.7257 
F-statistic:  3313 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4673 -0.6227 -0.1746  0.4966  6.4457 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.794233   0.054208  14.652  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.827844   0.094326  -8.776  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.002516   0.056302 -17.806  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.305239   0.028845  10.582  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.281952   0.029017   9.717  < 2e-16 ***
AF_25K_GIFT                          0.590150   0.043280  13.636  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255730   0.006747  37.900  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.932488   0.042323  69.289  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563296   0.025577  22.023  < 2e-16 ***
MG_250K_PLUS                         1.170852   0.070181  16.683  < 2e-16 ***
Alumnus                             -0.547835   0.021003 -26.083  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.260647   0.061926  -4.209 2.58e-05 ***
AFFINITY_SCORE                       0.156398   0.006797  23.009  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.115665   0.022130  -5.227 1.74e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.215982   0.028380   7.610 2.87e-14 ***
MG_PR_MODEL_DESCTop Tier             0.610057   0.028365  21.507  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9629 on 18762 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.726 
F-statistic:  3317 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3871 -0.6252 -0.1771  0.4974  6.4730 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.823498   0.054453  15.123  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.935844   0.094617  -9.891  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.049151   0.059789 -17.548  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.323109   0.028847  11.201  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.266102   0.028946   9.193  < 2e-16 ***
AF_25K_GIFT                          0.615100   0.042833  14.360  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258487   0.006770  38.181  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.936436   0.042413  69.234  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.569205   0.025671  22.173  < 2e-16 ***
MG_250K_PLUS                         1.091971   0.071254  15.325  < 2e-16 ***
Alumnus                             -0.554642   0.021014 -26.394  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.279709   0.060750  -4.604 4.17e-06 ***
AFFINITY_SCORE                       0.158344   0.006826  23.196  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.102352   0.022198  -4.611 4.04e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.215190   0.028496   7.552 4.50e-14 ***
MG_PR_MODEL_DESCTop Tier             0.604325   0.028375  21.298  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.966 on 18762 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7246 
F-statistic:  3295 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3911 -0.6266 -0.1792  0.5018  6.4486 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.831722   0.054424  15.282  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.900222   0.094559  -9.520  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.030774   0.059967 -17.189  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.323220   0.029019  11.138  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.290462   0.028990  10.019  < 2e-16 ***
AF_25K_GIFT                          0.627988   0.042984  14.610  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255054   0.006780  37.618  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.946733   0.042381  69.529  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.583634   0.025580  22.816  < 2e-16 ***
MG_250K_PLUS                         1.094495   0.071542  15.299  < 2e-16 ***
Alumnus                             -0.551041   0.021051 -26.176  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.273942   0.061081  -4.485 7.34e-06 ***
AFFINITY_SCORE                       0.153343   0.006836  22.433  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116602   0.022256  -5.239 1.63e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.199945   0.028581   6.996 2.73e-12 ***
MG_PR_MODEL_DESCTop Tier             0.585906   0.028422  20.614  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.967 on 18762 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7229 
F-statistic:  3267 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1809 -0.6296 -0.1774  0.5016  6.4453 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.807472   0.054715  14.758  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.871426   0.094932  -9.179  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.033357   0.060586 -17.056  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.343734   0.029141  11.796  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.278964   0.029192   9.556  < 2e-16 ***
AF_25K_GIFT                          0.609923   0.043130  14.142  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.254474   0.006783  37.516  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.937241   0.042599  68.950  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.564534   0.025759  21.916  < 2e-16 ***
MG_250K_PLUS                         1.098349   0.072010  15.253  < 2e-16 ***
Alumnus                             -0.552682   0.021115 -26.175  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.285556   0.062061  -4.601 4.23e-06 ***
AFFINITY_SCORE                       0.156475   0.006851  22.841  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108758   0.022328  -4.871 1.12e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.229200   0.028560   8.025 1.07e-15 ***
MG_PR_MODEL_DESCTop Tier             0.604290   0.028444  21.245  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9701 on 18762 degrees of freedom
Multiple R-squared:  0.7218,    Adjusted R-squared:  0.7216 
F-statistic:  3246 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4267 -0.6229 -0.1770  0.4995  6.4662 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.814980   0.054150  15.050  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.876078   0.094159  -9.304  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.078449   0.060033 -17.964  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.288239   0.029196   9.872  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.304469   0.028955  10.515  < 2e-16 ***
AF_25K_GIFT                          0.616901   0.043578  14.156  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255506   0.006773  37.725  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.935560   0.042461  69.136  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567033   0.025593  22.156  < 2e-16 ***
MG_250K_PLUS                         1.139479   0.072400  15.739  < 2e-16 ***
Alumnus                             -0.556020   0.020934 -26.560  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263161   0.061085  -4.308 1.65e-05 ***
AFFINITY_SCORE                       0.156750   0.006828  22.955  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123186   0.022226  -5.542 3.02e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.200280   0.028489   7.030 2.14e-12 ***
MG_PR_MODEL_DESCTop Tier             0.577337   0.028318  20.388  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9648 on 18762 degrees of freedom
Multiple R-squared:  0.7241,    Adjusted R-squared:  0.7238 
F-statistic:  3282 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3877 -0.6282 -0.1762  0.5003  6.4626 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.817828   0.054304  15.060  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.927772   0.094183  -9.851  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.051088   0.060242 -17.448  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.309881   0.028999  10.686  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300669   0.028964  10.381  < 2e-16 ***
AF_25K_GIFT                          0.627178   0.043022  14.578  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258529   0.006770  38.185  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.920189   0.042451  68.790  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.573120   0.025634  22.358  < 2e-16 ***
MG_250K_PLUS                         1.094343   0.072764  15.040  < 2e-16 ***
Alumnus                             -0.548623   0.021022 -26.097  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.262082   0.062326  -4.205 2.62e-05 ***
AFFINITY_SCORE                       0.155583   0.006829  22.782  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.102004   0.022249  -4.585 4.58e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.220011   0.028524   7.713 1.29e-14 ***
MG_PR_MODEL_DESCTop Tier             0.588639   0.028369  20.749  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.966 on 18762 degrees of freedom
Multiple R-squared:  0.7234,    Adjusted R-squared:  0.7232 
F-statistic:  3272 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4421 -0.6277 -0.1758  0.4993  6.4392 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.791753   0.054557  14.512  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.840441   0.094864  -8.859  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.038232   0.059933 -17.323  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.321922   0.029248  11.007  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288644   0.029164   9.897  < 2e-16 ***
AF_25K_GIFT                          0.581669   0.043773  13.288  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.254095   0.006818  37.271  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.921771   0.042527  68.704  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.560944   0.025801  21.742  < 2e-16 ***
MG_250K_PLUS                         1.134699   0.072459  15.660  < 2e-16 ***
Alumnus                             -0.546798   0.021124 -25.885  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.261045   0.061661  -4.234 2.31e-05 ***
AFFINITY_SCORE                       0.157941   0.006859  23.026  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109091   0.022287  -4.895 9.92e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.208235   0.028597   7.282 3.43e-13 ***
MG_PR_MODEL_DESCTop Tier             0.595443   0.028455  20.926  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9704 on 18762 degrees of freedom
Multiple R-squared:  0.7205,    Adjusted R-squared:  0.7203 
F-statistic:  3225 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3770 -0.6226 -0.1755  0.4943  6.4604 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.773305   0.054446  14.203  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.825544   0.094585  -8.728  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.039617   0.060370 -17.221  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.328862   0.028878  11.388  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277902   0.028990   9.586  < 2e-16 ***
AF_25K_GIFT                          0.572696   0.043148  13.273  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255514   0.006794  37.611  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.933427   0.042659  68.765  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582673   0.025647  22.719  < 2e-16 ***
MG_250K_PLUS                         1.075064   0.071779  14.977  < 2e-16 ***
Alumnus                             -0.548794   0.021032 -26.094  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.307039   0.061330  -5.006 5.60e-07 ***
AFFINITY_SCORE                       0.157850   0.006824  23.131  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111074   0.022318  -4.977 6.52e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.216703   0.028682   7.555 4.36e-14 ***
MG_PR_MODEL_DESCTop Tier             0.598574   0.028339  21.122  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9675 on 18762 degrees of freedom
Multiple R-squared:  0.7242,    Adjusted R-squared:  0.724 
F-statistic:  3285 on 15 and 18762 DF,  p-value: < 2.2e-16


[[2]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3377 -0.6311 -0.1801  0.5051  6.4433 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.790300   0.054442  14.516  < 2e-16 ***
ns(NUMERIC_AGE, df = s)1            -0.843606   0.094468  -8.930  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2            -1.025463   0.060041 -17.079  < 2e-16 ***
PM_VISIT_LAST_2_YRS                  0.315214   0.028961  10.884  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.285010   0.029116   9.789  < 2e-16 ***
AF_25K_GIFT                          0.616050   0.043010  14.323  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.250747   0.006795  36.903  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.939793   0.042600  69.010  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.588759   0.025699  22.910  < 2e-16 ***
MG_250K_PLUS                         1.066056   0.072102  14.785  < 2e-16 ***
Alumnus                             -0.545074   0.021089 -25.847  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.296644   0.062056  -4.780 1.76e-06 ***
AFFINITY_SCORE                       0.159183   0.006837  23.282  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.105552   0.022381  -4.716 2.42e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.210380   0.028660   7.341 2.21e-13 ***
MG_PR_MODEL_DESCTop Tier             0.586396   0.028444  20.616  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9696 on 18758 degrees of freedom
Multiple R-squared:  0.7219,    Adjusted R-squared:  0.7217 
F-statistic:  3247 on 15 and 18758 DF,  p-value: < 2.2e-16



[[3]]
[[3]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4360 -0.6219 -0.1800  0.4941  5.4672 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.358075   0.072991   4.906 9.39e-07 ***
ns(NUMERIC_AGE, df = s)1            -0.452759   0.044995 -10.062  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.282335   0.154973   1.822   0.0685 .  
ns(NUMERIC_AGE, df = s)3            -0.522346   0.087683  -5.957 2.61e-09 ***
PM_VISIT_LAST_2_YRS                  0.312085   0.028734  10.861  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274577   0.028877   9.509  < 2e-16 ***
AF_25K_GIFT                          0.614415   0.042560  14.436  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261445   0.006767  38.636  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929279   0.042295  69.258  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557954   0.025497  21.883  < 2e-16 ***
MG_250K_PLUS                         1.126097   0.071608  15.726  < 2e-16 ***
Alumnus                             -0.567458   0.021038 -26.973  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.262321   0.061201  -4.286 1.83e-05 ***
AFFINITY_SCORE                       0.158268   0.006792  23.301  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118449   0.022125  -5.353 8.73e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.213531   0.028342   7.534 5.14e-14 ***
MG_PR_MODEL_DESCTop Tier             0.594237   0.028189  21.080  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9611 on 18761 degrees of freedom
Multiple R-squared:  0.727, Adjusted R-squared:  0.7268 
F-statistic:  3123 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4752 -0.6194 -0.1824  0.4939  6.2696 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.360563   0.073273   4.921 8.69e-07 ***
ns(NUMERIC_AGE, df = s)1            -0.429283   0.044473  -9.653  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.317415   0.154324   2.057   0.0397 *  
ns(NUMERIC_AGE, df = s)3            -0.501935   0.080845  -6.209 5.46e-10 ***
PM_VISIT_LAST_2_YRS                  0.302292   0.028789  10.500  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279324   0.028960   9.645  < 2e-16 ***
AF_25K_GIFT                          0.587303   0.043194  13.597  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259958   0.006751  38.506  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913936   0.042290  68.903  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557904   0.025533  21.850  < 2e-16 ***
MG_250K_PLUS                         1.167391   0.070040  16.668  < 2e-16 ***
Alumnus                             -0.567248   0.021076 -26.914  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.255994   0.061803  -4.142 3.46e-05 ***
AFFINITY_SCORE                       0.158445   0.006788  23.344  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123704   0.022104  -5.596 2.22e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.207412   0.028340   7.319 2.60e-13 ***
MG_PR_MODEL_DESCTop Tier             0.599301   0.028334  21.151  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.961 on 18761 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.7271 
F-statistic:  3127 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3990 -0.6241 -0.1847  0.4941  6.3025 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.381157   0.074184   5.138 2.81e-07 ***
ns(NUMERIC_AGE, df = s)1            -0.478745   0.045335 -10.560  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.251596   0.157475   1.598     0.11    
ns(NUMERIC_AGE, df = s)3            -0.513003   0.086761  -5.913 3.42e-09 ***
PM_VISIT_LAST_2_YRS                  0.320032   0.028791  11.116  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263114   0.028890   9.108  < 2e-16 ***
AF_25K_GIFT                          0.613361   0.042747  14.348  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263096   0.006777  38.823  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.916887   0.042387  68.816  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563482   0.025628  21.987  < 2e-16 ***
MG_250K_PLUS                         1.090068   0.071111  15.329  < 2e-16 ***
Alumnus                             -0.573975   0.021087 -27.220  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275191   0.060630  -4.539 5.69e-06 ***
AFFINITY_SCORE                       0.160298   0.006816  23.517  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110686   0.022174  -4.992 6.04e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.206903   0.028454   7.271 3.70e-13 ***
MG_PR_MODEL_DESCTop Tier             0.594414   0.028340  20.974  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.964 on 18761 degrees of freedom
Multiple R-squared:  0.7259,    Adjusted R-squared:  0.7257 
F-statistic:  3106 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4057 -0.6229 -0.1866  0.4963  6.2590 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.378194   0.072866   5.190 2.12e-07 ***
ns(NUMERIC_AGE, df = s)1            -0.492308   0.045010 -10.938  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.326394   0.154835   2.108    0.035 *  
ns(NUMERIC_AGE, df = s)3            -0.443501   0.087746  -5.054 4.36e-07 ***
PM_VISIT_LAST_2_YRS                  0.319655   0.028955  11.040  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288018   0.028925   9.957  < 2e-16 ***
AF_25K_GIFT                          0.624199   0.042887  14.554  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259949   0.006785  38.313  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.925810   0.042344  69.097  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.578616   0.025527  22.667  < 2e-16 ***
MG_250K_PLUS                         1.093725   0.071378  15.323  < 2e-16 ***
Alumnus                             -0.570607   0.021107 -27.034  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.268315   0.060945  -4.403 1.08e-05 ***
AFFINITY_SCORE                       0.155470   0.006824  22.784  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.124777   0.022222  -5.615 1.99e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.191738   0.028529   6.721 1.86e-11 ***
MG_PR_MODEL_DESCTop Tier             0.575477   0.028379  20.278  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9648 on 18761 degrees of freedom
Multiple R-squared:  0.7244,    Adjusted R-squared:  0.7242 
F-statistic:  3082 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1005 -0.6261 -0.1846  0.4979  6.2612 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.376616   0.073279   5.139 2.78e-07 ***
ns(NUMERIC_AGE, df = s)1            -0.472476   0.045194 -10.454  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.294999   0.155954   1.892   0.0586 .  
ns(NUMERIC_AGE, df = s)3            -0.467416   0.088882  -5.259 1.47e-07 ***
PM_VISIT_LAST_2_YRS                  0.339512   0.029085  11.673  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277188   0.029134   9.514  < 2e-16 ***
AF_25K_GIFT                          0.606429   0.043044  14.089  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259101   0.006790  38.162  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917672   0.042570  68.537  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.559171   0.025713  21.746  < 2e-16 ***
MG_250K_PLUS                         1.097861   0.071863  15.277  < 2e-16 ***
Alumnus                             -0.571250   0.021177 -26.976  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.284340   0.061934  -4.591 4.44e-06 ***
AFFINITY_SCORE                       0.158559   0.006841  23.179  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116854   0.022302  -5.240 1.63e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.220258   0.028520   7.723 1.19e-14 ***
MG_PR_MODEL_DESCTop Tier             0.593793   0.028411  20.900  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18761 degrees of freedom
Multiple R-squared:  0.723, Adjusted R-squared:  0.7227 
F-statistic:  3060 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4436 -0.6202 -0.1833  0.4942  6.2679 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.346070   0.072294   4.787 1.71e-06 ***
ns(NUMERIC_AGE, df = s)1            -0.485810   0.044860 -10.830  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.393332   0.154097   2.552   0.0107 *  
ns(NUMERIC_AGE, df = s)3            -0.457639   0.087711  -5.218 1.83e-07 ***
PM_VISIT_LAST_2_YRS                  0.284077   0.029127   9.753  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301108   0.028885  10.424  < 2e-16 ***
AF_25K_GIFT                          0.613375   0.043470  14.110  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260695   0.006777  38.469  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912663   0.042419  68.664  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.561869   0.025535  22.004  < 2e-16 ***
MG_250K_PLUS                         1.141220   0.072219  15.802  < 2e-16 ***
Alumnus                             -0.576644   0.020988 -27.475  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259579   0.060933  -4.260 2.05e-05 ***
AFFINITY_SCORE                       0.158847   0.006815  23.309  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.133122   0.022194  -5.998 2.03e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.190989   0.028434   6.717 1.91e-11 ***
MG_PR_MODEL_DESCTop Tier             0.565182   0.028275  19.989  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9624 on 18761 degrees of freedom
Multiple R-squared:  0.7254,    Adjusted R-squared:  0.7252 
F-statistic:  3098 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4000 -0.6259 -0.1864  0.4954  6.2695 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.348360   0.073295   4.753 2.02e-06 ***
ns(NUMERIC_AGE, df = s)1            -0.489848   0.045300 -10.813  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.342897   0.155895   2.200   0.0279 *  
ns(NUMERIC_AGE, df = s)3            -0.458526   0.087544  -5.238 1.64e-07 ***
PM_VISIT_LAST_2_YRS                  0.305501   0.028934  10.559  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299215   0.028896  10.355  < 2e-16 ***
AF_25K_GIFT                          0.623214   0.042922  14.520  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263296   0.006773  38.874  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899372   0.042406  68.371  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567615   0.025580  22.190  < 2e-16 ***
MG_250K_PLUS                         1.093032   0.072591  15.057  < 2e-16 ***
Alumnus                             -0.570306   0.021095 -27.035  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259438   0.062179  -4.172 3.03e-05 ***
AFFINITY_SCORE                       0.157740   0.006817  23.140  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110059   0.022212  -4.955 7.30e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.211672   0.028469   7.435 1.09e-13 ***
MG_PR_MODEL_DESCTop Tier             0.577682   0.028325  20.395  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18761 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7245 
F-statistic:  3088 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4509 -0.6266 -0.1805  0.4972  6.2585 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.328595   0.073978   4.442 8.97e-06 ***
ns(NUMERIC_AGE, df = s)1            -0.442751   0.045512  -9.728  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.396283   0.157093   2.523   0.0117 *  
ns(NUMERIC_AGE, df = s)3            -0.460994   0.086824  -5.310 1.11e-07 ***
PM_VISIT_LAST_2_YRS                  0.318523   0.029185  10.914  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.286935   0.029099   9.861  < 2e-16 ***
AF_25K_GIFT                          0.576223   0.043679  13.192  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259068   0.006824  37.967  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901589   0.042487  68.293  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555584   0.025749  21.577  < 2e-16 ***
MG_250K_PLUS                         1.128282   0.072299  15.606  < 2e-16 ***
Alumnus                             -0.567554   0.021195 -26.778  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256884   0.061524  -4.175 2.99e-05 ***
AFFINITY_SCORE                       0.159774   0.006847  23.336  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117972   0.022257  -5.300 1.17e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.199455   0.028548   6.987 2.91e-12 ***
MG_PR_MODEL_DESCTop Tier             0.584461   0.028416  20.568  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18761 degrees of freedom
Multiple R-squared:  0.7218,    Adjusted R-squared:  0.7216 
F-statistic:  3042 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3881 -0.6208 -0.1851  0.4939  6.2782 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.321066   0.073758   4.353 1.35e-05 ***
ns(NUMERIC_AGE, df = s)1            -0.433469   0.045387  -9.551  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.383908   0.156806   2.448   0.0144 *  
ns(NUMERIC_AGE, df = s)3            -0.467241   0.087481  -5.341 9.35e-08 ***
PM_VISIT_LAST_2_YRS                  0.325143   0.028819  11.282  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276145   0.028928   9.546  < 2e-16 ***
AF_25K_GIFT                          0.568122   0.043058  13.194  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260068   0.006798  38.259  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.914326   0.042619  68.381  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.577044   0.025600  22.541  < 2e-16 ***
MG_250K_PLUS                         1.072515   0.071624  14.974  < 2e-16 ***
Alumnus                             -0.568569   0.021099 -26.948  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.302728   0.061199  -4.947 7.62e-07 ***
AFFINITY_SCORE                       0.159860   0.006813  23.464  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.120211   0.022292  -5.393 7.03e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.207551   0.028637   7.248 4.41e-13 ***
MG_PR_MODEL_DESCTop Tier             0.588070   0.028302  20.779  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9655 on 18761 degrees of freedom
Multiple R-squared:  0.7254,    Adjusted R-squared:  0.7252 
F-statistic:  3098 on 16 and 18761 DF,  p-value: < 2.2e-16


[[3]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3493 -0.6303 -0.1879  0.4979  6.2689 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.351584   0.073557   4.780 1.77e-06 ***
ns(NUMERIC_AGE, df = s)1            -0.442558   0.045391  -9.750  < 2e-16 ***
ns(NUMERIC_AGE, df = s)2             0.331759   0.155961   2.127   0.0334 *  
ns(NUMERIC_AGE, df = s)3            -0.476504   0.086788  -5.490 4.06e-08 ***
PM_VISIT_LAST_2_YRS                  0.311911   0.028904  10.791  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.283395   0.029057   9.753  < 2e-16 ***
AF_25K_GIFT                          0.611193   0.042926  14.238  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255408   0.006801  37.553  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.919516   0.042574  68.575  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582768   0.025655  22.715  < 2e-16 ***
MG_250K_PLUS                         1.063065   0.071955  14.774  < 2e-16 ***
Alumnus                             -0.564946   0.021164 -26.693  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.292387   0.061931  -4.721 2.36e-06 ***
AFFINITY_SCORE                       0.161271   0.006827  23.622  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.113356   0.022352  -5.071 3.99e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.202266   0.028615   7.068 1.62e-12 ***
MG_PR_MODEL_DESCTop Tier             0.576232   0.028409  20.284  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9676 on 18757 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7228 
F-statistic:  3061 on 16 and 18757 DF,  p-value: < 2.2e-16



[[4]]
[[4]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4296 -0.6231 -0.1840  0.4961  5.4705 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.288923   0.082811   3.489 0.000486 ***
ns(NUMERIC_AGE, df = s)1             0.288136   0.078848   3.654 0.000259 ***
ns(NUMERIC_AGE, df = s)2            -0.379694   0.062161  -6.108 1.03e-09 ***
ns(NUMERIC_AGE, df = s)3             0.417619   0.181241   2.304 0.021221 *  
ns(NUMERIC_AGE, df = s)4            -0.550520   0.101193  -5.440 5.39e-08 ***
PM_VISIT_LAST_2_YRS                  0.311668   0.028734  10.847  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274296   0.028877   9.499  < 2e-16 ***
AF_25K_GIFT                          0.615566   0.042565  14.462  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260932   0.006777  38.503  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929497   0.042296  69.261  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558140   0.025498  21.889  < 2e-16 ***
MG_250K_PLUS                         1.125450   0.071607  15.717  < 2e-16 ***
Alumnus                             -0.571979   0.021385 -26.747  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263322   0.061203  -4.302 1.70e-05 ***
AFFINITY_SCORE                       0.158169   0.006795  23.279  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118092   0.022125  -5.338 9.53e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.214319   0.028343   7.562 4.17e-14 ***
MG_PR_MODEL_DESCTop Tier             0.594900   0.028190  21.103  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9611 on 18760 degrees of freedom
Multiple R-squared:  0.7271,    Adjusted R-squared:  0.7268 
F-statistic:  2940 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4732 -0.6200 -0.1825  0.4953  6.2757 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.323824   0.081663   3.965 7.36e-05 ***
ns(NUMERIC_AGE, df = s)1             0.252712   0.077499   3.261  0.00111 ** 
ns(NUMERIC_AGE, df = s)2            -0.396870   0.061448  -6.459 1.08e-10 ***
ns(NUMERIC_AGE, df = s)3             0.378713   0.178456   2.122  0.03384 *  
ns(NUMERIC_AGE, df = s)4            -0.497885   0.094029  -5.295 1.20e-07 ***
PM_VISIT_LAST_2_YRS                  0.302180   0.028790  10.496  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279260   0.028961   9.643  < 2e-16 ***
AF_25K_GIFT                          0.587723   0.043197  13.606  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259750   0.006764  38.404  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913928   0.042296  68.894  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557857   0.025536  21.846  < 2e-16 ***
MG_250K_PLUS                         1.167091   0.070044  16.662  < 2e-16 ***
Alumnus                             -0.568612   0.021419 -26.547  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256391   0.061805  -4.148 3.36e-05 ***
AFFINITY_SCORE                       0.158480   0.006790  23.340  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123524   0.022104  -5.588 2.33e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.207792   0.028342   7.332 2.37e-13 ***
MG_PR_MODEL_DESCTop Tier             0.599653   0.028335  21.163  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.961 on 18760 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.7271 
F-statistic:  2943 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3985 -0.6242 -0.1847  0.4942  6.3032 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.354833   0.083365   4.256 2.09e-05 ***
ns(NUMERIC_AGE, df = s)1             0.220002   0.079432   2.770  0.00562 ** 
ns(NUMERIC_AGE, df = s)2            -0.458367   0.062425  -7.343 2.18e-13 ***
ns(NUMERIC_AGE, df = s)3             0.289234   0.182753   1.583  0.11352    
ns(NUMERIC_AGE, df = s)4            -0.495662   0.100754  -4.920 8.75e-07 ***
PM_VISIT_LAST_2_YRS                  0.319973   0.028793  11.113  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263120   0.028892   9.107  < 2e-16 ***
AF_25K_GIFT                          0.613658   0.042755  14.353  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263002   0.006788  38.745  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.916600   0.042391  68.803  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563353   0.025631  21.980  < 2e-16 ***
MG_250K_PLUS                         1.089883   0.071115  15.326  < 2e-16 ***
Alumnus                             -0.574169   0.021431 -26.791  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275382   0.060633  -4.542 5.61e-06 ***
AFFINITY_SCORE                       0.160376   0.006819  23.518  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110567   0.022175  -4.986 6.21e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207151   0.028457   7.279 3.48e-13 ***
MG_PR_MODEL_DESCTop Tier             0.594695   0.028342  20.983  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.964 on 18760 degrees of freedom
Multiple R-squared:  0.726, Adjusted R-squared:  0.7257 
F-statistic:  2923 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4017 -0.6248 -0.1849  0.4958  6.2695 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.325034   0.082395   3.945 8.01e-05 ***
ns(NUMERIC_AGE, df = s)1             0.261160   0.078116   3.343  0.00083 ***
ns(NUMERIC_AGE, df = s)2            -0.430002   0.062840  -6.843 8.00e-12 ***
ns(NUMERIC_AGE, df = s)3             0.425571   0.180091   2.363  0.01813 *  
ns(NUMERIC_AGE, df = s)4            -0.447974   0.102477  -4.371 1.24e-05 ***
PM_VISIT_LAST_2_YRS                  0.319487   0.028956  11.034  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287788   0.028927   9.949  < 2e-16 ***
AF_25K_GIFT                          0.625000   0.042894  14.571  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259612   0.006795  38.204  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.925819   0.042346  69.093  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.578604   0.025528  22.665  < 2e-16 ***
MG_250K_PLUS                         1.092912   0.071386  15.310  < 2e-16 ***
Alumnus                             -0.573106   0.021456 -26.710  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.268937   0.060945  -4.413 1.03e-05 ***
AFFINITY_SCORE                       0.155503   0.006825  22.783  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.124479   0.022222  -5.602 2.15e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.192303   0.028530   6.740 1.62e-11 ***
MG_PR_MODEL_DESCTop Tier             0.576021   0.028380  20.297  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9648 on 18760 degrees of freedom
Multiple R-squared:  0.7244,    Adjusted R-squared:  0.7242 
F-statistic:  2901 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1051 -0.6283 -0.1865  0.5004  6.2701 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.329943   0.082904   3.980 6.92e-05 ***
ns(NUMERIC_AGE, df = s)1             0.249292   0.078665   3.169  0.00153 ** 
ns(NUMERIC_AGE, df = s)2            -0.419939   0.063256  -6.639 3.25e-11 ***
ns(NUMERIC_AGE, df = s)3             0.376821   0.181407   2.077  0.03780 *  
ns(NUMERIC_AGE, df = s)4            -0.469290   0.103865  -4.518 6.27e-06 ***
PM_VISIT_LAST_2_YRS                  0.339327   0.029087  11.666  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277025   0.029135   9.508  < 2e-16 ***
AF_25K_GIFT                          0.607102   0.043050  14.102  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258789   0.006802  38.049  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917760   0.042572  68.537  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.559183   0.025716  21.745  < 2e-16 ***
MG_250K_PLUS                         1.097380   0.071869  15.269  < 2e-16 ***
Alumnus                             -0.573278   0.021539 -26.616  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.284668   0.061935  -4.596 4.33e-06 ***
AFFINITY_SCORE                       0.158588   0.006842  23.177  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116625   0.022302  -5.229 1.72e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.220733   0.028521   7.739 1.05e-14 ***
MG_PR_MODEL_DESCTop Tier             0.594226   0.028412  20.915  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18760 degrees of freedom
Multiple R-squared:  0.723, Adjusted R-squared:  0.7227 
F-statistic:  2880 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4377 -0.6197 -0.1862  0.4938  6.2851 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.284823   0.081713   3.486 0.000492 ***
ns(NUMERIC_AGE, df = s)1             0.308956   0.077609   3.981 6.89e-05 ***
ns(NUMERIC_AGE, df = s)2            -0.409983   0.062611  -6.548 5.98e-11 ***
ns(NUMERIC_AGE, df = s)3             0.507758   0.179007   2.837 0.004566 ** 
ns(NUMERIC_AGE, df = s)4            -0.478039   0.102672  -4.656 3.25e-06 ***
PM_VISIT_LAST_2_YRS                  0.283734   0.029128   9.741  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300959   0.028885  10.419  < 2e-16 ***
AF_25K_GIFT                          0.614436   0.043477  14.133  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260220   0.006787  38.342  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913102   0.042423  68.667  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562084   0.025537  22.010  < 2e-16 ***
MG_250K_PLUS                         1.140005   0.072229  15.783  < 2e-16 ***
Alumnus                             -0.580115   0.021340 -27.184  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.260423   0.060935  -4.274 1.93e-05 ***
AFFINITY_SCORE                       0.158786   0.006818  23.291  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.132685   0.022194  -5.978 2.29e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.191778   0.028436   6.744 1.58e-11 ***
MG_PR_MODEL_DESCTop Tier             0.565939   0.028276  20.015  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9624 on 18760 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7252 
F-statistic:  2916 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4001 -0.6257 -0.1871  0.4949  6.2676 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.322857   0.081648   3.954 7.71e-05 ***
ns(NUMERIC_AGE, df = s)1             0.242376   0.077371   3.133  0.00174 ** 
ns(NUMERIC_AGE, df = s)2            -0.463822   0.062721  -7.395 1.47e-13 ***
ns(NUMERIC_AGE, df = s)3             0.379954   0.178521   2.128  0.03332 *  
ns(NUMERIC_AGE, df = s)4            -0.436114   0.103878  -4.198 2.70e-05 ***
PM_VISIT_LAST_2_YRS                  0.305488   0.028935  10.558  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299286   0.028897  10.357  < 2e-16 ***
AF_25K_GIFT                          0.623444   0.042928  14.523  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263227   0.006783  38.806  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899179   0.042410  68.361  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567471   0.025584  22.181  < 2e-16 ***
MG_250K_PLUS                         1.092909   0.072594  15.055  < 2e-16 ***
Alumnus                             -0.570159   0.021442 -26.590  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259581   0.062181  -4.175 3.00e-05 ***
AFFINITY_SCORE                       0.157842   0.006820  23.144  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109932   0.022213  -4.949 7.52e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.211905   0.028473   7.442 1.03e-13 ***
MG_PR_MODEL_DESCTop Tier             0.577901   0.028327  20.401  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18760 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7245 
F-statistic:  2906 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4496 -0.6251 -0.1805  0.4962  6.2614 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.294159   0.082738   3.555 0.000378 ***
ns(NUMERIC_AGE, df = s)1             0.288964   0.078520   3.680 0.000234 ***
ns(NUMERIC_AGE, df = s)2            -0.409295   0.063072  -6.489 8.84e-11 ***
ns(NUMERIC_AGE, df = s)3             0.451616   0.181119   2.493 0.012658 *  
ns(NUMERIC_AGE, df = s)4            -0.449143   0.102079  -4.400 1.09e-05 ***
PM_VISIT_LAST_2_YRS                  0.318483   0.029185  10.913  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.286940   0.029100   9.860  < 2e-16 ***
AF_25K_GIFT                          0.576557   0.043684  13.198  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258906   0.006835  37.880  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901343   0.042490  68.283  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555492   0.025751  21.572  < 2e-16 ***
MG_250K_PLUS                         1.127942   0.072302  15.600  < 2e-16 ***
Alumnus                             -0.568424   0.021556 -26.370  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.257084   0.061526  -4.178 2.95e-05 ***
AFFINITY_SCORE                       0.159856   0.006848  23.342  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117802   0.022258  -5.293 1.22e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.199750   0.028550   6.997 2.71e-12 ***
MG_PR_MODEL_DESCTop Tier             0.584714   0.028417  20.576  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18760 degrees of freedom
Multiple R-squared:  0.7218,    Adjusted R-squared:  0.7216 
F-statistic:  2863 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3855 -0.6218 -0.1867  0.4930  6.2866 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.280355   0.082526   3.397 0.000682 ***
ns(NUMERIC_AGE, df = s)1             0.299286   0.078611   3.807 0.000141 ***
ns(NUMERIC_AGE, df = s)2            -0.393943   0.062216  -6.332 2.48e-10 ***
ns(NUMERIC_AGE, df = s)3             0.454727   0.180833   2.515 0.011924 *  
ns(NUMERIC_AGE, df = s)4            -0.465830   0.102074  -4.564 5.06e-06 ***
PM_VISIT_LAST_2_YRS                  0.324971   0.028819  11.276  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276117   0.028929   9.545  < 2e-16 ***
AF_25K_GIFT                          0.568679   0.043064  13.206  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259833   0.006809  38.162  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.914313   0.042623  68.374  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.576984   0.025602  22.537  < 2e-16 ***
MG_250K_PLUS                         1.072120   0.071627  14.968  < 2e-16 ***
Alumnus                             -0.570335   0.021460 -26.577  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.303062   0.061201  -4.952 7.41e-07 ***
AFFINITY_SCORE                       0.159883   0.006815  23.460  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.119966   0.022293  -5.381 7.48e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.207905   0.028638   7.260 4.03e-13 ***
MG_PR_MODEL_DESCTop Tier             0.588369   0.028301  20.790  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9655 on 18760 degrees of freedom
Multiple R-squared:  0.7254,    Adjusted R-squared:  0.7252 
F-statistic:  2916 on 17 and 18760 DF,  p-value: < 2.2e-16


[[4]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3449 -0.6305 -0.1886  0.5002  6.2836 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.299770   0.081998   3.656 0.000257 ***
ns(NUMERIC_AGE, df = s)1             0.270641   0.077680   3.484 0.000495 ***
ns(NUMERIC_AGE, df = s)2            -0.386445   0.062683  -6.165 7.19e-10 ***
ns(NUMERIC_AGE, df = s)3             0.431885   0.179201   2.410 0.015960 *  
ns(NUMERIC_AGE, df = s)4            -0.495068   0.102271  -4.841 1.30e-06 ***
PM_VISIT_LAST_2_YRS                  0.311617   0.028905  10.781  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.283248   0.029057   9.748  < 2e-16 ***
AF_25K_GIFT                          0.611953   0.042930  14.255  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255020   0.006813  37.430  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.919673   0.042576  68.575  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582794   0.025656  22.715  < 2e-16 ***
MG_250K_PLUS                         1.062659   0.071954  14.769  < 2e-16 ***
Alumnus                             -0.568056   0.021513 -26.406  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.292723   0.061930  -4.727 2.30e-06 ***
AFFINITY_SCORE                       0.161245   0.006829  23.612  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.113029   0.022352  -5.057 4.31e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.202817   0.028616   7.087 1.41e-12 ***
MG_PR_MODEL_DESCTop Tier             0.576788   0.028410  20.302  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9676 on 18756 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7229 
F-statistic:  2881 on 17 and 18756 DF,  p-value: < 2.2e-16



[[5]]
[[5]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4291 -0.6237 -0.1842  0.4958  5.4709 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.255115   0.094156   2.709  0.00675 ** 
ns(NUMERIC_AGE, df = s)1             0.386056   0.086031   4.487 7.25e-06 ***
ns(NUMERIC_AGE, df = s)2             0.250456   0.100715   2.487  0.01290 *  
ns(NUMERIC_AGE, df = s)3            -0.347147   0.069289  -5.010 5.49e-07 ***
ns(NUMERIC_AGE, df = s)4             0.464897   0.213109   2.181  0.02916 *  
ns(NUMERIC_AGE, df = s)5            -0.546338   0.114141  -4.787 1.71e-06 ***
PM_VISIT_LAST_2_YRS                  0.311628   0.028735  10.845  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274347   0.028878   9.500  < 2e-16 ***
AF_25K_GIFT                          0.615943   0.042566  14.470  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260803   0.006777  38.485  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929400   0.042294  69.262  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558071   0.025498  21.887  < 2e-16 ***
MG_250K_PLUS                         1.125296   0.071607  15.715  < 2e-16 ***
Alumnus                             -0.572103   0.021390 -26.746  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263700   0.061203  -4.309 1.65e-05 ***
AFFINITY_SCORE                       0.158247   0.006798  23.279  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117862   0.022128  -5.326 1.01e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.214713   0.028350   7.574 3.80e-14 ***
MG_PR_MODEL_DESCTop Tier             0.595287   0.028195  21.113  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9611 on 18759 degrees of freedom
Multiple R-squared:  0.7271,    Adjusted R-squared:  0.7268 
F-statistic:  2776 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4727 -0.6214 -0.1825  0.4944  6.2743 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.283317   0.093030   3.045  0.00233 ** 
ns(NUMERIC_AGE, df = s)1             0.359258   0.085039   4.225 2.40e-05 ***
ns(NUMERIC_AGE, df = s)2             0.230563   0.099705   2.312  0.02076 *  
ns(NUMERIC_AGE, df = s)3            -0.369434   0.067707  -5.456 4.92e-08 ***
ns(NUMERIC_AGE, df = s)4             0.450814   0.209545   2.151  0.03146 *  
ns(NUMERIC_AGE, df = s)5            -0.482863   0.104079  -4.639 3.52e-06 ***
PM_VISIT_LAST_2_YRS                  0.302190   0.028790  10.496  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279258   0.028960   9.643  < 2e-16 ***
AF_25K_GIFT                          0.588082   0.043198  13.614  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259590   0.006763  38.383  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913908   0.042293  68.898  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557706   0.025538  21.839  < 2e-16 ***
MG_250K_PLUS                         1.166991   0.070045  16.661  < 2e-16 ***
Alumnus                             -0.568739   0.021427 -26.543  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256878   0.061807  -4.156 3.25e-05 ***
AFFINITY_SCORE                       0.158592   0.006794  23.343  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123273   0.022106  -5.576 2.49e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208271   0.028348   7.347 2.11e-13 ***
MG_PR_MODEL_DESCTop Tier             0.600171   0.028343  21.175  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.961 on 18759 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.7271 
F-statistic:  2780 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3972 -0.6247 -0.1861  0.4948  6.3062 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.331319   0.095104   3.484 0.000496 ***
ns(NUMERIC_AGE, df = s)1             0.310424   0.086897   3.572 0.000355 ***
ns(NUMERIC_AGE, df = s)2             0.161271   0.101850   1.583 0.113343    
ns(NUMERIC_AGE, df = s)3            -0.422841   0.069459  -6.088 1.17e-09 ***
ns(NUMERIC_AGE, df = s)4             0.320024   0.215424   1.486 0.137415    
ns(NUMERIC_AGE, df = s)5            -0.497399   0.113736  -4.373 1.23e-05 ***
PM_VISIT_LAST_2_YRS                  0.319925   0.028793  11.111  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263066   0.028893   9.105  < 2e-16 ***
AF_25K_GIFT                          0.613941   0.042757  14.359  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262874   0.006788  38.726  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.916592   0.042389  68.805  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563381   0.025631  21.980  < 2e-16 ***
MG_250K_PLUS                         1.089595   0.071116  15.321  < 2e-16 ***
Alumnus                             -0.574934   0.021439 -26.818  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275646   0.060635  -4.546 5.50e-06 ***
AFFINITY_SCORE                       0.160383   0.006823  23.506  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110457   0.022177  -4.981 6.39e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207385   0.028463   7.286 3.32e-13 ***
MG_PR_MODEL_DESCTop Tier             0.594950   0.028351  20.985  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.964 on 18759 degrees of freedom
Multiple R-squared:  0.726, Adjusted R-squared:  0.7257 
F-statistic:  2761 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4015 -0.6252 -0.1848  0.4979  6.2642 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.274694   0.093894   2.926 0.003442 ** 
ns(NUMERIC_AGE, df = s)1             0.375171   0.085767   4.374 1.22e-05 ***
ns(NUMERIC_AGE, df = s)2             0.250632   0.100418   2.496 0.012573 *  
ns(NUMERIC_AGE, df = s)3            -0.405832   0.069359  -5.851 4.96e-09 ***
ns(NUMERIC_AGE, df = s)4             0.526632   0.212578   2.477 0.013245 *  
ns(NUMERIC_AGE, df = s)5            -0.418507   0.114422  -3.658 0.000255 ***
PM_VISIT_LAST_2_YRS                  0.319594   0.028956  11.037  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287850   0.028926   9.951  < 2e-16 ***
AF_25K_GIFT                          0.625386   0.042895  14.579  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259398   0.006796  38.169  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.925863   0.042344  69.098  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.578451   0.025528  22.659  < 2e-16 ***
MG_250K_PLUS                         1.092788   0.071386  15.308  < 2e-16 ***
Alumnus                             -0.572718   0.021470 -26.675  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.269615   0.060948  -4.424 9.76e-06 ***
AFFINITY_SCORE                       0.155681   0.006829  22.797  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.124106   0.022225  -5.584 2.38e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.192985   0.028538   6.762 1.40e-11 ***
MG_PR_MODEL_DESCTop Tier             0.576692   0.028387  20.315  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9648 on 18759 degrees of freedom
Multiple R-squared:  0.7244,    Adjusted R-squared:  0.7242 
F-statistic:  2740 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1069 -0.6275 -0.1859  0.4998  6.2731 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.304096   0.094649   3.213  0.00132 ** 
ns(NUMERIC_AGE, df = s)1             0.349770   0.086479   4.045 5.26e-05 ***
ns(NUMERIC_AGE, df = s)2             0.205294   0.101354   2.026  0.04283 *  
ns(NUMERIC_AGE, df = s)3            -0.385610   0.069824  -5.523 3.38e-08 ***
ns(NUMERIC_AGE, df = s)4             0.411601   0.214714   1.917  0.05526 .  
ns(NUMERIC_AGE, df = s)5            -0.467467   0.116063  -4.028 5.65e-05 ***
PM_VISIT_LAST_2_YRS                  0.339281   0.029088  11.664  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276994   0.029135   9.507  < 2e-16 ***
AF_25K_GIFT                          0.607389   0.043052  14.108  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258651   0.006802  38.026  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917820   0.042572  68.539  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.559188   0.025716  21.745  < 2e-16 ***
MG_250K_PLUS                         1.097125   0.071871  15.265  < 2e-16 ***
Alumnus                             -0.573888   0.021552 -26.628  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.284875   0.061937  -4.599 4.26e-06 ***
AFFINITY_SCORE                       0.158616   0.006845  23.171  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116487   0.022304  -5.223 1.78e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221006   0.028528   7.747 9.88e-15 ***
MG_PR_MODEL_DESCTop Tier             0.594491   0.028418  20.920  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18759 degrees of freedom
Multiple R-squared:  0.723, Adjusted R-squared:  0.7227 
F-statistic:  2720 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4361 -0.6200 -0.1867  0.4945  6.2946 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.278270   0.093230   2.985  0.00284 ** 
ns(NUMERIC_AGE, df = s)1             0.410210   0.085208   4.814 1.49e-06 ***
ns(NUMERIC_AGE, df = s)2             0.233923   0.099899   2.342  0.01921 *  
ns(NUMERIC_AGE, df = s)3            -0.361744   0.069187  -5.229 1.73e-07 ***
ns(NUMERIC_AGE, df = s)4             0.484563   0.211617   2.290  0.02204 *  
ns(NUMERIC_AGE, df = s)5            -0.497700   0.114815  -4.335 1.47e-05 ***
PM_VISIT_LAST_2_YRS                  0.283623   0.029129   9.737  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300906   0.028885  10.417  < 2e-16 ***
AF_25K_GIFT                          0.614608   0.043478  14.136  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260185   0.006788  38.331  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913073   0.042421  68.670  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562183   0.025537  22.014  < 2e-16 ***
MG_250K_PLUS                         1.139511   0.072230  15.776  < 2e-16 ***
Alumnus                             -0.581322   0.021355 -27.222  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.260444   0.060938  -4.274 1.93e-05 ***
AFFINITY_SCORE                       0.158698   0.006821  23.265  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.132689   0.022198  -5.978 2.31e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.191770   0.028444   6.742 1.61e-11 ***
MG_PR_MODEL_DESCTop Tier             0.565934   0.028285  20.008  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9624 on 18759 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7252 
F-statistic:  2754 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3994 -0.6266 -0.1874  0.4958  6.2636 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.283086   0.093027   3.043 0.002345 ** 
ns(NUMERIC_AGE, df = s)1             0.349749   0.084895   4.120 3.81e-05 ***
ns(NUMERIC_AGE, df = s)2             0.214313   0.099606   2.152 0.031441 *  
ns(NUMERIC_AGE, df = s)3            -0.438830   0.069369  -6.326 2.57e-10 ***
ns(NUMERIC_AGE, df = s)4             0.456633   0.211028   2.164 0.030489 *  
ns(NUMERIC_AGE, df = s)5            -0.411888   0.116315  -3.541 0.000399 ***
PM_VISIT_LAST_2_YRS                  0.305546   0.028936  10.560  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299356   0.028897  10.359  < 2e-16 ***
AF_25K_GIFT                          0.623798   0.042930  14.531  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263053   0.006784  38.776  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899286   0.042408  68.366  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567406   0.025584  22.179  < 2e-16 ***
MG_250K_PLUS                         1.092580   0.072594  15.051  < 2e-16 ***
Alumnus                             -0.570026   0.021460 -26.562  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259960   0.062182  -4.181 2.92e-05 ***
AFFINITY_SCORE                       0.157965   0.006824  23.149  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109675   0.022215  -4.937 8.01e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.212359   0.028477   7.457 9.23e-14 ***
MG_PR_MODEL_DESCTop Tier             0.578368   0.028332  20.414  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18759 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7245 
F-statistic:  2745 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4493 -0.6252 -0.1804  0.4957  6.2555 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.242819   0.094508   2.569 0.010198 *  
ns(NUMERIC_AGE, df = s)1             0.400320   0.086354   4.636 3.58e-06 ***
ns(NUMERIC_AGE, df = s)2             0.280377   0.101216   2.770 0.005610 ** 
ns(NUMERIC_AGE, df = s)3            -0.386612   0.069558  -5.558 2.76e-08 ***
ns(NUMERIC_AGE, df = s)4             0.554027   0.214110   2.588 0.009673 ** 
ns(NUMERIC_AGE, df = s)5            -0.417324   0.114021  -3.660 0.000253 ***
PM_VISIT_LAST_2_YRS                  0.318603   0.029185  10.917  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287104   0.029101   9.866  < 2e-16 ***
AF_25K_GIFT                          0.576878   0.043685  13.206  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258682   0.006836  37.844  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901273   0.042488  68.284  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555337   0.025751  21.565  < 2e-16 ***
MG_250K_PLUS                         1.127600   0.072302  15.596  < 2e-16 ***
Alumnus                             -0.568027   0.021564 -26.341  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.257464   0.061525  -4.185 2.87e-05 ***
AFFINITY_SCORE                       0.160057   0.006853  23.356  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117462   0.022260  -5.277 1.33e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200356   0.028555   7.016 2.35e-12 ***
MG_PR_MODEL_DESCTop Tier             0.585297   0.028422  20.593  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9682 on 18759 degrees of freedom
Multiple R-squared:  0.7218,    Adjusted R-squared:  0.7216 
F-statistic:  2704 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3846 -0.6212 -0.1874  0.4930  6.2869 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.240996   0.093787   2.570   0.0102 *  
ns(NUMERIC_AGE, df = s)1             0.398169   0.085670   4.648 3.38e-06 ***
ns(NUMERIC_AGE, df = s)2             0.265551   0.100441   2.644   0.0082 ** 
ns(NUMERIC_AGE, df = s)3            -0.358989   0.069381  -5.174 2.31e-07 ***
ns(NUMERIC_AGE, df = s)4             0.521546   0.212440   2.455   0.0141 *  
ns(NUMERIC_AGE, df = s)5            -0.457178   0.115156  -3.970 7.21e-05 ***
PM_VISIT_LAST_2_YRS                  0.324927   0.028819  11.275  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276194   0.028930   9.547  < 2e-16 ***
AF_25K_GIFT                          0.569050   0.043065  13.214  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259652   0.006808  38.138  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.914343   0.042620  68.379  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.576876   0.025602  22.532  < 2e-16 ***
MG_250K_PLUS                         1.071882   0.071627  14.965  < 2e-16 ***
Alumnus                             -0.570608   0.021474 -26.572  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.303422   0.061201  -4.958 7.19e-07 ***
AFFINITY_SCORE                       0.159975   0.006819  23.460  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.119662   0.022296  -5.367 8.10e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208362   0.028645   7.274 3.63e-13 ***
MG_PR_MODEL_DESCTop Tier             0.588830   0.028309  20.800  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9655 on 18759 degrees of freedom
Multiple R-squared:  0.7255,    Adjusted R-squared:  0.7252 
F-statistic:  2754 on 18 and 18759 DF,  p-value: < 2.2e-16


[[5]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3449 -0.6302 -0.1870  0.5008  6.2783 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.245968   0.093356   2.635  0.00843 ** 
ns(NUMERIC_AGE, df = s)1             0.385387   0.085217   4.522 6.15e-06 ***
ns(NUMERIC_AGE, df = s)2             0.270300   0.099870   2.707  0.00681 ** 
ns(NUMERIC_AGE, df = s)3            -0.364235   0.069246  -5.260 1.46e-07 ***
ns(NUMERIC_AGE, df = s)4             0.538473   0.211455   2.547  0.01089 *  
ns(NUMERIC_AGE, df = s)5            -0.465707   0.114036  -4.084 4.45e-05 ***
PM_VISIT_LAST_2_YRS                  0.311610   0.028905  10.781  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.283431   0.029058   9.754  < 2e-16 ***
AF_25K_GIFT                          0.612449   0.042930  14.266  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.254815   0.006813  37.400  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.919716   0.042574  68.580  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582585   0.025656  22.708  < 2e-16 ***
MG_250K_PLUS                         1.062557   0.071953  14.767  < 2e-16 ***
Alumnus                             -0.567552   0.021520 -26.373  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.293029   0.061929  -4.732 2.24e-06 ***
AFFINITY_SCORE                       0.161414   0.006832  23.628  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.112613   0.022355  -5.037 4.76e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.203528   0.028624   7.111 1.20e-12 ***
MG_PR_MODEL_DESCTop Tier             0.577537   0.028417  20.323  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9675 on 18755 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7229 
F-statistic:  2721 on 18 and 18755 DF,  p-value: < 2.2e-16



[[6]]
[[6]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4417 -0.6241 -0.1817  0.4929  5.4637 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.161509   0.102127   1.581  0.11379    
ns(NUMERIC_AGE, df = s)1             0.449311   0.090479   4.966 6.90e-07 ***
ns(NUMERIC_AGE, df = s)2             0.445612   0.102831   4.333 1.48e-05 ***
ns(NUMERIC_AGE, df = s)3             0.244518   0.103602   2.360  0.01828 *  
ns(NUMERIC_AGE, df = s)4            -0.227344   0.081194  -2.800  0.00512 ** 
ns(NUMERIC_AGE, df = s)5             0.623163   0.226471   2.752  0.00594 ** 
ns(NUMERIC_AGE, df = s)6            -0.627560   0.124148  -5.055 4.35e-07 ***
PM_VISIT_LAST_2_YRS                  0.311088   0.028731  10.827  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.275203   0.028876   9.530  < 2e-16 ***
AF_25K_GIFT                          0.616886   0.042562  14.494  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261364   0.006781  38.544  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.924384   0.042341  69.067  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555818   0.025512  21.787  < 2e-16 ***
MG_250K_PLUS                         1.125455   0.071597  15.719  < 2e-16 ***
Alumnus                             -0.563399   0.021700 -25.963  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263292   0.061196  -4.302 1.70e-05 ***
AFFINITY_SCORE                       0.159102   0.006806  23.378  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117204   0.022126  -5.297 1.19e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.214947   0.028347   7.583 3.54e-14 ***
MG_PR_MODEL_DESCTop Tier             0.596042   0.028194  21.141  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9609 on 18758 degrees of freedom
Multiple R-squared:  0.7272,    Adjusted R-squared:  0.7269 
F-statistic:  2631 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4882 -0.6226 -0.1777  0.4910  6.3082 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.140629   0.102735   1.369  0.17106    
ns(NUMERIC_AGE, df = s)1             0.453043   0.091284   4.963 7.00e-07 ***
ns(NUMERIC_AGE, df = s)2             0.495750   0.104207   4.757 1.98e-06 ***
ns(NUMERIC_AGE, df = s)3             0.248431   0.103865   2.392  0.01677 *  
ns(NUMERIC_AGE, df = s)4            -0.210035   0.079848  -2.630  0.00853 ** 
ns(NUMERIC_AGE, df = s)5             0.717798   0.226054   3.175  0.00150 ** 
ns(NUMERIC_AGE, df = s)6            -0.564545   0.112837  -5.003 5.69e-07 ***
PM_VISIT_LAST_2_YRS                  0.301663   0.028782  10.481  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.280519   0.028954   9.688  < 2e-16 ***
AF_25K_GIFT                          0.589883   0.043189  13.658  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260352   0.006766  38.478  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.907212   0.042331  68.678  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.554858   0.025543  21.722  < 2e-16 ***
MG_250K_PLUS                         1.166120   0.070024  16.653  < 2e-16 ***
Alumnus                             -0.557103   0.021692 -25.682  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.255797   0.061791  -4.140 3.49e-05 ***
AFFINITY_SCORE                       0.159680   0.006798  23.488  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.122390   0.022102  -5.538 3.11e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208786   0.028342   7.367 1.82e-13 ***
MG_PR_MODEL_DESCTop Tier             0.601227   0.028338  21.216  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9607 on 18758 degrees of freedom
Multiple R-squared:  0.7275,    Adjusted R-squared:  0.7272 
F-statistic:  2636 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4112 -0.6254 -0.1831  0.4952  6.3333 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.205039   0.105406   1.945 0.051763 .  
ns(NUMERIC_AGE, df = s)1             0.399056   0.093598   4.264 2.02e-05 ***
ns(NUMERIC_AGE, df = s)2             0.415436   0.106898   3.886 0.000102 ***
ns(NUMERIC_AGE, df = s)3             0.176561   0.106375   1.660 0.096972 .  
ns(NUMERIC_AGE, df = s)4            -0.278527   0.082249  -3.386 0.000710 ***
ns(NUMERIC_AGE, df = s)5             0.550571   0.232668   2.366 0.017975 *  
ns(NUMERIC_AGE, df = s)6            -0.577993   0.123692  -4.673 2.99e-06 ***
PM_VISIT_LAST_2_YRS                  0.319663   0.028788  11.104  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263825   0.028888   9.133  < 2e-16 ***
AF_25K_GIFT                          0.615479   0.042751  14.397  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263509   0.006792  38.798  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.910252   0.042439  68.575  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.560671   0.025643  21.865  < 2e-16 ***
MG_250K_PLUS                         1.089131   0.071102  15.318  < 2e-16 ***
Alumnus                             -0.565002   0.021702 -26.034  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275058   0.060624  -4.537 5.74e-06 ***
AFFINITY_SCORE                       0.161395   0.006829  23.633  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109735   0.022174  -4.949 7.53e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207843   0.028459   7.303 2.92e-13 ***
MG_PR_MODEL_DESCTop Tier             0.596083   0.028348  21.027  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9639 on 18758 degrees of freedom
Multiple R-squared:  0.7261,    Adjusted R-squared:  0.7258 
F-statistic:  2617 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4164 -0.6254 -0.1826  0.4979  6.2955 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.155837   0.102731   1.517  0.12930    
ns(NUMERIC_AGE, df = s)1             0.455519   0.090985   5.007 5.59e-07 ***
ns(NUMERIC_AGE, df = s)2             0.469975   0.103308   4.549 5.42e-06 ***
ns(NUMERIC_AGE, df = s)3             0.242309   0.104399   2.321  0.02030 *  
ns(NUMERIC_AGE, df = s)4            -0.248052   0.082820  -2.995  0.00275 ** 
ns(NUMERIC_AGE, df = s)5             0.736255   0.227884   3.231  0.00124 ** 
ns(NUMERIC_AGE, df = s)6            -0.522828   0.126773  -4.124 3.74e-05 ***
PM_VISIT_LAST_2_YRS                  0.318796   0.028951  11.011  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288959   0.028923   9.991  < 2e-16 ***
AF_25K_GIFT                          0.626774   0.042888  14.614  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260093   0.006800  38.246  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.919263   0.042401  68.848  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.575730   0.025540  22.542  < 2e-16 ***
MG_250K_PLUS                         1.092521   0.071370  15.308  < 2e-16 ***
Alumnus                             -0.562627   0.021762 -25.854  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.268438   0.060938  -4.405 1.06e-05 ***
AFFINITY_SCORE                       0.156739   0.006837  22.926  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123505   0.022222  -5.558 2.77e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.193407   0.028533   6.778 1.25e-11 ***
MG_PR_MODEL_DESCTop Tier             0.577673   0.028384  20.352  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9646 on 18758 degrees of freedom
Multiple R-squared:  0.7246,    Adjusted R-squared:  0.7243 
F-statistic:  2597 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1155 -0.6278 -0.1842  0.4990  6.3037 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.188382   0.103797   1.815  0.06955 .  
ns(NUMERIC_AGE, df = s)1             0.424115   0.091864   4.617 3.92e-06 ***
ns(NUMERIC_AGE, df = s)2             0.432669   0.104605   4.136 3.55e-05 ***
ns(NUMERIC_AGE, df = s)3             0.206241   0.105209   1.960  0.04998 *  
ns(NUMERIC_AGE, df = s)4            -0.240483   0.082253  -2.924  0.00346 ** 
ns(NUMERIC_AGE, df = s)5             0.616767   0.230280   2.678  0.00741 ** 
ns(NUMERIC_AGE, df = s)6            -0.564864   0.126432  -4.468 7.95e-06 ***
PM_VISIT_LAST_2_YRS                  0.338502   0.029083  11.639  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.278173   0.029132   9.549  < 2e-16 ***
AF_25K_GIFT                          0.608798   0.043045  14.143  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259287   0.006806  38.099  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911506   0.042624  68.307  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.556232   0.025731  21.617  < 2e-16 ***
MG_250K_PLUS                         1.096862   0.071855  15.265  < 2e-16 ***
Alumnus                             -0.563400   0.021863 -25.769  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.283062   0.061929  -4.571 4.89e-06 ***
AFFINITY_SCORE                       0.159678   0.006853  23.300  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.115959   0.022300  -5.200 2.02e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221331   0.028523   7.760 8.95e-15 ***
MG_PR_MODEL_DESCTop Tier             0.595406   0.028414  20.954  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.968 on 18758 degrees of freedom
Multiple R-squared:  0.7231,    Adjusted R-squared:  0.7228 
F-statistic:  2578 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4513 -0.6222 -0.1835  0.4958  6.3237 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.165886   0.102120   1.624  0.10430    
ns(NUMERIC_AGE, df = s)1             0.484415   0.090411   5.358 8.52e-08 ***
ns(NUMERIC_AGE, df = s)2             0.472897   0.102911   4.595 4.35e-06 ***
ns(NUMERIC_AGE, df = s)3             0.236019   0.103611   2.278  0.02274 *  
ns(NUMERIC_AGE, df = s)4            -0.221179   0.081433  -2.716  0.00661 ** 
ns(NUMERIC_AGE, df = s)5             0.678408   0.226636   2.993  0.00276 ** 
ns(NUMERIC_AGE, df = s)6            -0.588336   0.125270  -4.697 2.66e-06 ***
PM_VISIT_LAST_2_YRS                  0.283328   0.029123   9.729  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.302050   0.028883  10.458  < 2e-16 ***
AF_25K_GIFT                          0.615960   0.043472  14.169  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260861   0.006792  38.405  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.907219   0.042468  68.457  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.559504   0.025550  21.898  < 2e-16 ***
MG_250K_PLUS                         1.139550   0.072215  15.780  < 2e-16 ***
Alumnus                             -0.571090   0.021664 -26.361  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259378   0.060929  -4.257 2.08e-05 ***
AFFINITY_SCORE                       0.159629   0.006827  23.382  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131877   0.022196  -5.942 2.87e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.192339   0.028440   6.763 1.39e-11 ***
MG_PR_MODEL_DESCTop Tier             0.567138   0.028285  20.051  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9622 on 18758 degrees of freedom
Multiple R-squared:  0.7256,    Adjusted R-squared:  0.7253 
F-statistic:  2611 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4131 -0.6255 -0.1844  0.4966  6.2897 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.165449   0.102705   1.611 0.107215    
ns(NUMERIC_AGE, df = s)1             0.438551   0.091123   4.813 1.50e-06 ***
ns(NUMERIC_AGE, df = s)2             0.448963   0.104016   4.316 1.59e-05 ***
ns(NUMERIC_AGE, df = s)3             0.225724   0.103765   2.175 0.029617 *  
ns(NUMERIC_AGE, df = s)4            -0.300647   0.081916  -3.670 0.000243 ***
ns(NUMERIC_AGE, df = s)5             0.667750   0.226961   2.942 0.003263 ** 
ns(NUMERIC_AGE, df = s)6            -0.484865   0.127072  -3.816 0.000136 ***
PM_VISIT_LAST_2_YRS                  0.305441   0.028931  10.558  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300279   0.028894  10.392  < 2e-16 ***
AF_25K_GIFT                          0.624477   0.042922  14.549  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263622   0.006788  38.836  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.893931   0.042455  68.165  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.564849   0.025598  22.066  < 2e-16 ***
MG_250K_PLUS                         1.092780   0.072582  15.056  < 2e-16 ***
Alumnus                             -0.561241   0.021711 -25.851  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259038   0.062174  -4.166 3.11e-05 ***
AFFINITY_SCORE                       0.158836   0.006829  23.258  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108647   0.022214  -4.891 1.01e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.213204   0.028475   7.488 7.33e-14 ***
MG_PR_MODEL_DESCTop Tier             0.579905   0.028333  20.467  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9635 on 18758 degrees of freedom
Multiple R-squared:  0.7249,    Adjusted R-squared:  0.7246 
F-statistic:  2602 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4624 -0.6277 -0.1807  0.4960  6.2873 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.117734   0.104527   1.126 0.260034    
ns(NUMERIC_AGE, df = s)1             0.492262   0.092844   5.302 1.16e-07 ***
ns(NUMERIC_AGE, df = s)2             0.517367   0.105802   4.890 1.02e-06 ***
ns(NUMERIC_AGE, df = s)3             0.278630   0.105841   2.633 0.008482 ** 
ns(NUMERIC_AGE, df = s)4            -0.221484   0.083447  -2.654 0.007957 ** 
ns(NUMERIC_AGE, df = s)5             0.769634   0.231079   3.331 0.000868 ***
ns(NUMERIC_AGE, df = s)6            -0.516069   0.126160  -4.091 4.32e-05 ***
PM_VISIT_LAST_2_YRS                  0.318214   0.029180  10.905  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287937   0.029096   9.896  < 2e-16 ***
AF_25K_GIFT                          0.578544   0.043678  13.246  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259358   0.006840  37.920  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.895109   0.042535  68.063  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.552872   0.025760  21.462  < 2e-16 ***
MG_250K_PLUS                         1.126702   0.072287  15.587  < 2e-16 ***
Alumnus                             -0.558070   0.021841 -25.552  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256914   0.061514  -4.177 2.97e-05 ***
AFFINITY_SCORE                       0.160958   0.006858  23.471  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116829   0.022257  -5.249 1.55e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200768   0.028551   7.032 2.11e-12 ***
MG_PR_MODEL_DESCTop Tier             0.586164   0.028419  20.626  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.968 on 18758 degrees of freedom
Multiple R-squared:  0.722, Adjusted R-squared:  0.7217 
F-statistic:  2564 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4004 -0.6227 -0.1836  0.4913  6.3203 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.108097   0.103590   1.044 0.296725    
ns(NUMERIC_AGE, df = s)1             0.488379   0.091969   5.310 1.11e-07 ***
ns(NUMERIC_AGE, df = s)2             0.522123   0.104958   4.975 6.60e-07 ***
ns(NUMERIC_AGE, df = s)3             0.275644   0.104661   2.634 0.008454 ** 
ns(NUMERIC_AGE, df = s)4            -0.195021   0.081942  -2.380 0.017324 *  
ns(NUMERIC_AGE, df = s)5             0.755605   0.228699   3.304 0.000955 ***
ns(NUMERIC_AGE, df = s)6            -0.556749   0.125394  -4.440 9.05e-06 ***
PM_VISIT_LAST_2_YRS                  0.324496   0.028812  11.262  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277191   0.028923   9.584  < 2e-16 ***
AF_25K_GIFT                          0.570775   0.043057  13.256  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260392   0.006812  38.227  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.907935   0.042658  68.168  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.574039   0.025610  22.414  < 2e-16 ***
MG_250K_PLUS                         1.072012   0.071608  14.971  < 2e-16 ***
Alumnus                             -0.559832   0.021726 -25.767  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.302490   0.061187  -4.944 7.73e-07 ***
AFFINITY_SCORE                       0.160985   0.006823  23.593  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118475   0.022293  -5.314 1.08e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.208956   0.028640   7.296 3.08e-13 ***
MG_PR_MODEL_DESCTop Tier             0.590000   0.028306  20.844  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9652 on 18758 degrees of freedom
Multiple R-squared:  0.7256,    Adjusted R-squared:  0.7253 
F-statistic:  2611 on 19 and 18758 DF,  p-value: < 2.2e-16


[[6]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3596 -0.6299 -0.1862  0.4999  6.3119 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.110230   0.103064   1.070 0.284844    
ns(NUMERIC_AGE, df = s)1             0.481905   0.091466   5.269 1.39e-07 ***
ns(NUMERIC_AGE, df = s)2             0.516898   0.104203   4.960 7.09e-07 ***
ns(NUMERIC_AGE, df = s)3             0.277045   0.104323   2.656 0.007922 ** 
ns(NUMERIC_AGE, df = s)4            -0.193612   0.082869  -2.336 0.019483 *  
ns(NUMERIC_AGE, df = s)5             0.776015   0.227918   3.405 0.000664 ***
ns(NUMERIC_AGE, df = s)6            -0.572936   0.126296  -4.536 5.76e-06 ***
PM_VISIT_LAST_2_YRS                  0.310653   0.028899  10.750  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.284572   0.029052   9.795  < 2e-16 ***
AF_25K_GIFT                          0.614159   0.042922  14.309  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255594   0.006818  37.491  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912890   0.042624  68.340  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.579588   0.025667  22.581  < 2e-16 ***
MG_250K_PLUS                         1.061861   0.071934  14.762  < 2e-16 ***
Alumnus                             -0.556824   0.021789 -25.556  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.292609   0.061913  -4.726 2.31e-06 ***
AFFINITY_SCORE                       0.162351   0.006835  23.752  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111699   0.022351  -4.997 5.86e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.204208   0.028618   7.136 9.99e-13 ***
MG_PR_MODEL_DESCTop Tier             0.578891   0.028414  20.373  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9673 on 18754 degrees of freedom
Multiple R-squared:  0.7233,    Adjusted R-squared:  0.723 
F-statistic:  2580 on 19 and 18754 DF,  p-value: < 2.2e-16



[[7]]
[[7]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4413 -0.6242 -0.1800  0.4921  5.4641 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.102258   0.108266   0.945  0.34492    
ns(NUMERIC_AGE, df = s)1             0.510971   0.095517   5.350 8.92e-08 ***
ns(NUMERIC_AGE, df = s)2             0.544020   0.120432   4.517 6.30e-06 ***
ns(NUMERIC_AGE, df = s)3             0.425155   0.106077   4.008 6.15e-05 ***
ns(NUMERIC_AGE, df = s)4             0.271595   0.114011   2.382  0.01722 *  
ns(NUMERIC_AGE, df = s)5            -0.180979   0.084863  -2.133  0.03297 *  
ns(NUMERIC_AGE, df = s)6             0.723239   0.242227   2.986  0.00283 ** 
ns(NUMERIC_AGE, df = s)7            -0.636151   0.132839  -4.789 1.69e-06 ***
PM_VISIT_LAST_2_YRS                  0.311329   0.028730  10.836  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.275127   0.028874   9.528  < 2e-16 ***
AF_25K_GIFT                          0.616899   0.042558  14.495  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261342   0.006780  38.543  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.924686   0.042339  69.077  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.556075   0.025513  21.796  < 2e-16 ***
MG_250K_PLUS                         1.125042   0.071603  15.712  < 2e-16 ***
Alumnus                             -0.563446   0.021681 -25.988  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.264132   0.061193  -4.316 1.59e-05 ***
AFFINITY_SCORE                       0.159029   0.006805  23.371  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116947   0.022125  -5.286 1.27e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.215291   0.028347   7.595 3.23e-14 ***
MG_PR_MODEL_DESCTop Tier             0.596571   0.028197  21.157  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9608 on 18757 degrees of freedom
Multiple R-squared:  0.7272,    Adjusted R-squared:  0.7269 
F-statistic:  2500 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4856 -0.6240 -0.1788  0.4903  6.3129 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.096716   0.106796   0.906 0.365152    
ns(NUMERIC_AGE, df = s)1             0.491068   0.094256   5.210 1.91e-07 ***
ns(NUMERIC_AGE, df = s)2             0.575352   0.119142   4.829 1.38e-06 ***
ns(NUMERIC_AGE, df = s)3             0.426741   0.104718   4.075 4.62e-05 ***
ns(NUMERIC_AGE, df = s)4             0.272398   0.112845   2.414 0.015792 *  
ns(NUMERIC_AGE, df = s)5            -0.185622   0.082800  -2.242 0.024984 *  
ns(NUMERIC_AGE, df = s)6             0.805318   0.237965   3.384 0.000715 ***
ns(NUMERIC_AGE, df = s)7            -0.565719   0.120186  -4.707 2.53e-06 ***
PM_VISIT_LAST_2_YRS                  0.301844   0.028780  10.488  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.280407   0.028953   9.685  < 2e-16 ***
AF_25K_GIFT                          0.589746   0.043186  13.656  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260258   0.006767  38.461  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.908368   0.042344  68.684  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555500   0.025551  21.741  < 2e-16 ***
MG_250K_PLUS                         1.165449   0.070030  16.642  < 2e-16 ***
Alumnus                             -0.558534   0.021734 -25.699  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256419   0.061788  -4.150 3.34e-05 ***
AFFINITY_SCORE                       0.159462   0.006800  23.449  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.122238   0.022101  -5.531 3.23e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.209046   0.028343   7.376 1.70e-13 ***
MG_PR_MODEL_DESCTop Tier             0.601631   0.028341  21.229  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9607 on 18757 degrees of freedom
Multiple R-squared:  0.7276,    Adjusted R-squared:  0.7273 
F-statistic:  2505 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4090 -0.6257 -0.1826  0.4936  6.3371 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.161157   0.109801   1.468  0.14220    
ns(NUMERIC_AGE, df = s)1             0.435524   0.096783   4.500 6.84e-06 ***
ns(NUMERIC_AGE, df = s)2             0.499929   0.122415   4.084 4.45e-05 ***
ns(NUMERIC_AGE, df = s)3             0.347658   0.107615   3.231  0.00124 ** 
ns(NUMERIC_AGE, df = s)4             0.199849   0.115648   1.728  0.08399 .  
ns(NUMERIC_AGE, df = s)5            -0.254522   0.085226  -2.986  0.00283 ** 
ns(NUMERIC_AGE, df = s)6             0.640322   0.245682   2.606  0.00916 ** 
ns(NUMERIC_AGE, df = s)7            -0.578993   0.132246  -4.378 1.20e-05 ***
PM_VISIT_LAST_2_YRS                  0.319923   0.028786  11.114  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263739   0.028887   9.130  < 2e-16 ***
AF_25K_GIFT                          0.615302   0.042748  14.394  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263402   0.006793  38.778  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911190   0.042451  68.577  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.561260   0.025648  21.883  < 2e-16 ***
MG_250K_PLUS                         1.088688   0.071105  15.311  < 2e-16 ***
Alumnus                             -0.566247   0.021732 -26.056  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.275669   0.060622  -4.547 5.47e-06 ***
AFFINITY_SCORE                       0.161209   0.006831  23.601  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109648   0.022173  -4.945 7.67e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.208058   0.028458   7.311 2.76e-13 ***
MG_PR_MODEL_DESCTop Tier             0.596277   0.028347  21.035  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9638 on 18757 degrees of freedom
Multiple R-squared:  0.7261,    Adjusted R-squared:  0.7258 
F-statistic:  2487 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4136 -0.6247 -0.1810  0.4969  6.2964 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.095146   0.107842   0.882 0.377642    
ns(NUMERIC_AGE, df = s)1             0.510990   0.095101   5.373 7.83e-08 ***
ns(NUMERIC_AGE, df = s)2             0.584610   0.120110   4.867 1.14e-06 ***
ns(NUMERIC_AGE, df = s)3             0.438237   0.105643   4.148 3.36e-05 ***
ns(NUMERIC_AGE, df = s)4             0.293098   0.113689   2.578 0.009943 ** 
ns(NUMERIC_AGE, df = s)5            -0.223607   0.084934  -2.633 0.008477 ** 
ns(NUMERIC_AGE, df = s)6             0.863976   0.241406   3.579 0.000346 ***
ns(NUMERIC_AGE, df = s)7            -0.498940   0.133433  -3.739 0.000185 ***
PM_VISIT_LAST_2_YRS                  0.319101   0.028948  11.023  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288949   0.028921   9.991  < 2e-16 ***
AF_25K_GIFT                          0.626451   0.042886  14.607  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259968   0.006800  38.231  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.920790   0.042410  68.871  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.576489   0.025543  22.569  < 2e-16 ***
MG_250K_PLUS                         1.091590   0.071369  15.295  < 2e-16 ***
Alumnus                             -0.563961   0.021742 -25.939  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.269352   0.060934  -4.420 9.91e-06 ***
AFFINITY_SCORE                       0.156521   0.006837  22.895  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123333   0.022220  -5.550 2.89e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.193742   0.028532   6.790 1.15e-11 ***
MG_PR_MODEL_DESCTop Tier             0.578168   0.028384  20.370  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9645 on 18757 degrees of freedom
Multiple R-squared:  0.7246,    Adjusted R-squared:  0.7243 
F-statistic:  2468 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1156 -0.6280 -0.1823  0.5007  6.3131 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.138251   0.109068   1.268 0.204969    
ns(NUMERIC_AGE, df = s)1             0.474572   0.096119   4.937 7.99e-07 ***
ns(NUMERIC_AGE, df = s)2             0.519096   0.121560   4.270 1.96e-05 ***
ns(NUMERIC_AGE, df = s)3             0.396831   0.106902   3.712 0.000206 ***
ns(NUMERIC_AGE, df = s)4             0.222147   0.114929   1.933 0.053262 .  
ns(NUMERIC_AGE, df = s)5            -0.193047   0.085581  -2.256 0.024100 *  
ns(NUMERIC_AGE, df = s)6             0.702642   0.244537   2.873 0.004066 ** 
ns(NUMERIC_AGE, df = s)7            -0.583393   0.135193  -4.315 1.60e-05 ***
PM_VISIT_LAST_2_YRS                  0.338703   0.029080  11.647  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.278390   0.029130   9.557  < 2e-16 ***
AF_25K_GIFT                          0.608615   0.043042  14.140  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259237   0.006805  38.095  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911844   0.042623  68.316  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.556515   0.025735  21.625  < 2e-16 ***
MG_250K_PLUS                         1.096550   0.071858  15.260  < 2e-16 ***
Alumnus                             -0.563687   0.021852 -25.795  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.283670   0.061925  -4.581 4.66e-06 ***
AFFINITY_SCORE                       0.159592   0.006853  23.288  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.115784   0.022299  -5.192 2.10e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221502   0.028524   7.766 8.55e-15 ***
MG_PR_MODEL_DESCTop Tier             0.595680   0.028414  20.964  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9679 on 18757 degrees of freedom
Multiple R-squared:  0.7232,    Adjusted R-squared:  0.7229 
F-statistic:  2450 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4494 -0.6206 -0.1827  0.4946  6.3287 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.102971   0.107197   0.961 0.336773    
ns(NUMERIC_AGE, df = s)1             0.536046   0.094521   5.671 1.44e-08 ***
ns(NUMERIC_AGE, df = s)2             0.600646   0.119553   5.024 5.10e-07 ***
ns(NUMERIC_AGE, df = s)3             0.431191   0.105093   4.103 4.10e-05 ***
ns(NUMERIC_AGE, df = s)4             0.279043   0.113074   2.468 0.013604 *  
ns(NUMERIC_AGE, df = s)5            -0.186792   0.084786  -2.203 0.027600 *  
ns(NUMERIC_AGE, df = s)6             0.803414   0.240417   3.342 0.000834 ***
ns(NUMERIC_AGE, df = s)7            -0.585442   0.134084  -4.366 1.27e-05 ***
PM_VISIT_LAST_2_YRS                  0.283919   0.029122   9.749  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301950   0.028880  10.455  < 2e-16 ***
AF_25K_GIFT                          0.615734   0.043468  14.165  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260794   0.006791  38.401  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.908517   0.042471  68.483  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.560276   0.025554  21.925  < 2e-16 ***
MG_250K_PLUS                         1.138830   0.072215  15.770  < 2e-16 ***
Alumnus                             -0.572302   0.021646 -26.440  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.260100   0.060922  -4.269 1.97e-05 ***
AFFINITY_SCORE                       0.159373   0.006827  23.344  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131517   0.022194  -5.926 3.16e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.192776   0.028439   6.779 1.25e-11 ***
MG_PR_MODEL_DESCTop Tier             0.567825   0.028286  20.075  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9621 on 18757 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7254 
F-statistic:  2481 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4118 -0.6261 -0.1835  0.4961  6.2949 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.117126   0.106783   1.097 0.272718    
ns(NUMERIC_AGE, df = s)1             0.477331   0.094085   5.073 3.95e-07 ***
ns(NUMERIC_AGE, df = s)2             0.537373   0.119035   4.514 6.39e-06 ***
ns(NUMERIC_AGE, df = s)3             0.392179   0.104574   3.750 0.000177 ***
ns(NUMERIC_AGE, df = s)4             0.246168   0.112685   2.185 0.028932 *  
ns(NUMERIC_AGE, df = s)5            -0.266616   0.084922  -3.140 0.001695 ** 
ns(NUMERIC_AGE, df = s)6             0.767429   0.239493   3.204 0.001356 ** 
ns(NUMERIC_AGE, df = s)7            -0.489834   0.136162  -3.597 0.000322 ***
PM_VISIT_LAST_2_YRS                  0.305753   0.028929  10.569  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.300163   0.028893  10.389  < 2e-16 ***
AF_25K_GIFT                          0.624247   0.042920  14.544  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263555   0.006789  38.821  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.894551   0.042470  68.154  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.565227   0.025606  22.074  < 2e-16 ***
MG_250K_PLUS                         1.092212   0.072591  15.046  < 2e-16 ***
Alumnus                             -0.561957   0.021735 -25.855  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259465   0.062170  -4.174 3.01e-05 ***
AFFINITY_SCORE                       0.158705   0.006831  23.232  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108367   0.022213  -4.878 1.08e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.213574   0.028475   7.501 6.64e-14 ***
MG_PR_MODEL_DESCTop Tier             0.580503   0.028335  20.487  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9634 on 18757 degrees of freedom
Multiple R-squared:  0.725, Adjusted R-squared:  0.7247 
F-statistic:  2472 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4619 -0.6286 -0.1796  0.4964  6.2923 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.08198    0.10884   0.753 0.451329    
ns(NUMERIC_AGE, df = s)1             0.52985    0.09593   5.523 3.38e-08 ***
ns(NUMERIC_AGE, df = s)2             0.57166    0.12144   4.707 2.53e-06 ***
ns(NUMERIC_AGE, df = s)3             0.45922    0.10667   4.305 1.68e-05 ***
ns(NUMERIC_AGE, df = s)4             0.29202    0.11478   2.544 0.010959 *  
ns(NUMERIC_AGE, df = s)5            -0.19541    0.08523  -2.293 0.021882 *  
ns(NUMERIC_AGE, df = s)6             0.83285    0.24372   3.417 0.000634 ***
ns(NUMERIC_AGE, df = s)7            -0.51625    0.13279  -3.888 0.000101 ***
PM_VISIT_LAST_2_YRS                  0.31838    0.02918  10.911  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.28788    0.02910   9.894  < 2e-16 ***
AF_25K_GIFT                          0.57845    0.04368  13.244  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.25934    0.00684  37.916  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.89551    0.04256  68.035  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.55313    0.02577  21.467  < 2e-16 ***
MG_250K_PLUS                         1.12652    0.07229  15.583  < 2e-16 ***
Alumnus                             -0.55834    0.02187 -25.527  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.25740    0.06151  -4.185 2.87e-05 ***
AFFINITY_SCORE                       0.16087    0.00686  23.450  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.11669    0.02226  -5.243 1.60e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.20092    0.02855   7.037 2.03e-12 ***
MG_PR_MODEL_DESCTop Tier             0.58645    0.02842  20.635  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.968 on 18757 degrees of freedom
Multiple R-squared:  0.722, Adjusted R-squared:  0.7217 
F-statistic:  2436 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4013 -0.6228 -0.1839  0.4910  6.3317 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.080150   0.107750   0.744  0.45698    
ns(NUMERIC_AGE, df = s)1             0.524258   0.094972   5.520 3.43e-08 ***
ns(NUMERIC_AGE, df = s)2             0.555303   0.120203   4.620 3.87e-06 ***
ns(NUMERIC_AGE, df = s)3             0.455051   0.105557   4.311 1.63e-05 ***
ns(NUMERIC_AGE, df = s)4             0.263956   0.113663   2.322  0.02023 *  
ns(NUMERIC_AGE, df = s)5            -0.151502   0.085004  -1.782  0.07472 .  
ns(NUMERIC_AGE, df = s)6             0.786124   0.241257   3.258  0.00112 ** 
ns(NUMERIC_AGE, df = s)7            -0.588816   0.134114  -4.390 1.14e-05 ***
PM_VISIT_LAST_2_YRS                  0.324652   0.028812  11.268  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277190   0.028922   9.584  < 2e-16 ***
AF_25K_GIFT                          0.570923   0.043056  13.260  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260401   0.006813  38.223  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.907835   0.042670  68.147  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.573952   0.025623  22.400  < 2e-16 ***
MG_250K_PLUS                         1.072566   0.071616  14.977  < 2e-16 ***
Alumnus                             -0.559668   0.021756 -25.724  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.302883   0.061185  -4.950 7.47e-07 ***
AFFINITY_SCORE                       0.160975   0.006825  23.585  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118377   0.022294  -5.310 1.11e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.208963   0.028641   7.296 3.09e-13 ***
MG_PR_MODEL_DESCTop Tier             0.590082   0.028307  20.845  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9652 on 18757 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7254 
F-statistic:  2481 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3582 -0.6307 -0.1864  0.5001  6.3150 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.065436   0.107155   0.611 0.541429    
ns(NUMERIC_AGE, df = s)1             0.522871   0.094443   5.536 3.13e-08 ***
ns(NUMERIC_AGE, df = s)2             0.590313   0.119517   4.939 7.91e-07 ***
ns(NUMERIC_AGE, df = s)3             0.461536   0.104972   4.397 1.10e-05 ***
ns(NUMERIC_AGE, df = s)4             0.307790   0.113032   2.723 0.006475 ** 
ns(NUMERIC_AGE, df = s)5            -0.174227   0.084702  -2.057 0.039707 *  
ns(NUMERIC_AGE, df = s)6             0.865603   0.240061   3.606 0.000312 ***
ns(NUMERIC_AGE, df = s)7            -0.564743   0.132928  -4.248 2.16e-05 ***
PM_VISIT_LAST_2_YRS                  0.310795   0.028897  10.755  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.284543   0.029051   9.795  < 2e-16 ***
AF_25K_GIFT                          0.614032   0.042920  14.306  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255531   0.006818  37.479  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913676   0.042640  68.332  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.580105   0.025678  22.592  < 2e-16 ***
MG_250K_PLUS                         1.061474   0.071937  14.756  < 2e-16 ***
Alumnus                             -0.557525   0.021817 -25.554  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.293179   0.061911  -4.736 2.20e-06 ***
AFFINITY_SCORE                       0.162192   0.006838  23.718  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111517   0.022350  -4.990 6.11e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.204501   0.028618   7.146 9.27e-13 ***
MG_PR_MODEL_DESCTop Tier             0.579304   0.028415  20.387  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9672 on 18753 degrees of freedom
Multiple R-squared:  0.7233,    Adjusted R-squared:  0.7231 
F-statistic:  2452 on 20 and 18753 DF,  p-value: < 2.2e-16



[[8]]
[[8]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4360 -0.6239 -0.1774  0.4913  5.4687 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.039417   0.113821   0.346  0.72911    
ns(NUMERIC_AGE, df = s)1             0.585910   0.102169   5.735 9.92e-09 ***
ns(NUMERIC_AGE, df = s)2             0.687613   0.129206   5.322 1.04e-07 ***
ns(NUMERIC_AGE, df = s)3             0.510361   0.114241   4.467 7.96e-06 ***
ns(NUMERIC_AGE, df = s)4             0.551116   0.121474   4.537 5.74e-06 ***
ns(NUMERIC_AGE, df = s)5             0.265351   0.119434   2.222  0.02631 *  
ns(NUMERIC_AGE, df = s)6            -0.082664   0.090404  -0.914  0.36053    
ns(NUMERIC_AGE, df = s)7             0.839434   0.255648   3.284  0.00103 ** 
ns(NUMERIC_AGE, df = s)8            -0.718695   0.142863  -5.031 4.93e-07 ***
PM_VISIT_LAST_2_YRS                  0.312134   0.028727  10.865  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274596   0.028870   9.511  < 2e-16 ***
AF_25K_GIFT                          0.616388   0.042553  14.485  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261012   0.006781  38.493  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.928066   0.042353  69.135  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557704   0.025517  21.856  < 2e-16 ***
MG_250K_PLUS                         1.127244   0.071594  15.745  < 2e-16 ***
Alumnus                             -0.568534   0.021779 -26.104  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.266337   0.061190  -4.353 1.35e-05 ***
AFFINITY_SCORE                       0.158313   0.006809  23.250  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117186   0.022122  -5.297 1.19e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.215217   0.028342   7.593 3.26e-14 ***
MG_PR_MODEL_DESCTop Tier             0.596246   0.028194  21.148  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9607 on 18756 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.727 
F-statistic:  2382 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4810 -0.6225 -0.1721  0.4904  6.3360 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.040003   0.112265   0.356 0.721597    
ns(NUMERIC_AGE, df = s)1             0.571004   0.100752   5.667 1.47e-08 ***
ns(NUMERIC_AGE, df = s)2             0.683037   0.127519   5.356 8.59e-08 ***
ns(NUMERIC_AGE, df = s)3             0.531198   0.112442   4.724 2.33e-06 ***
ns(NUMERIC_AGE, df = s)4             0.525181   0.120921   4.343 1.41e-05 ***
ns(NUMERIC_AGE, df = s)5             0.260089   0.118274   2.199 0.027887 *  
ns(NUMERIC_AGE, df = s)6            -0.103058   0.087377  -1.179 0.238228    
ns(NUMERIC_AGE, df = s)7             0.911108   0.251292   3.626 0.000289 ***
ns(NUMERIC_AGE, df = s)8            -0.624626   0.130205  -4.797 1.62e-06 ***
PM_VISIT_LAST_2_YRS                  0.302816   0.028782  10.521  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279913   0.028950   9.669  < 2e-16 ***
AF_25K_GIFT                          0.588782   0.043183  13.635  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259966   0.006767  38.415  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911550   0.042363  68.729  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.556767   0.025554  21.788  < 2e-16 ***
MG_250K_PLUS                         1.167345   0.070022  16.671  < 2e-16 ***
Alumnus                             -0.563052   0.021840 -25.781  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259006   0.061792  -4.192 2.78e-05 ***
AFFINITY_SCORE                       0.158868   0.006805  23.347  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.122321   0.022100  -5.535 3.15e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208697   0.028340   7.364 1.86e-13 ***
MG_PR_MODEL_DESCTop Tier             0.601444   0.028339  21.223  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9606 on 18756 degrees of freedom
Multiple R-squared:  0.7276,    Adjusted R-squared:  0.7273 
F-statistic:  2386 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4051 -0.6246 -0.1799  0.4914  6.3571 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.101223   0.115722   0.875 0.381744    
ns(NUMERIC_AGE, df = s)1             0.511486   0.103752   4.930 8.30e-07 ***
ns(NUMERIC_AGE, df = s)2             0.621729   0.131482   4.729 2.28e-06 ***
ns(NUMERIC_AGE, df = s)3             0.451576   0.116240   3.885 0.000103 ***
ns(NUMERIC_AGE, df = s)4             0.462241   0.123461   3.744 0.000182 ***
ns(NUMERIC_AGE, df = s)5             0.192943   0.121351   1.590 0.111862    
ns(NUMERIC_AGE, df = s)6            -0.160660   0.090896  -1.768 0.077157 .  
ns(NUMERIC_AGE, df = s)7             0.754015   0.259879   2.901 0.003719 ** 
ns(NUMERIC_AGE, df = s)8            -0.649946   0.142084  -4.574 4.81e-06 ***
PM_VISIT_LAST_2_YRS                  0.320887   0.028788  11.147  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263522   0.028884   9.124  < 2e-16 ***
AF_25K_GIFT                          0.614004   0.042748  14.363  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263089   0.006793  38.728  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.913994   0.042465  68.621  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562786   0.025655  21.936  < 2e-16 ***
MG_250K_PLUS                         1.090803   0.071099  15.342  < 2e-16 ***
Alumnus                             -0.570637   0.021839 -26.130  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.277679   0.060623  -4.580 4.67e-06 ***
AFFINITY_SCORE                       0.160614   0.006835  23.498  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.109727   0.022171  -4.949 7.52e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207893   0.028455   7.306 2.86e-13 ***
MG_PR_MODEL_DESCTop Tier             0.596034   0.028346  21.027  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18756 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7259 
F-statistic:  2369 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4087 -0.6246 -0.1777  0.4929  6.3194 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.036173   0.113345   0.319 0.749625    
ns(NUMERIC_AGE, df = s)1             0.593737   0.101664   5.840 5.30e-09 ***
ns(NUMERIC_AGE, df = s)2             0.698901   0.128636   5.433 5.61e-08 ***
ns(NUMERIC_AGE, df = s)3             0.541457   0.113456   4.772 1.83e-06 ***
ns(NUMERIC_AGE, df = s)4             0.545744   0.121760   4.482 7.43e-06 ***
ns(NUMERIC_AGE, df = s)5             0.275220   0.119179   2.309 0.020938 *  
ns(NUMERIC_AGE, df = s)6            -0.125082   0.089682  -1.395 0.163113    
ns(NUMERIC_AGE, df = s)7             0.970271   0.254893   3.807 0.000141 ***
ns(NUMERIC_AGE, df = s)8            -0.575917   0.145263  -3.965 7.38e-05 ***
PM_VISIT_LAST_2_YRS                  0.320326   0.028951  11.064  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288626   0.028917   9.981  < 2e-16 ***
AF_25K_GIFT                          0.626123   0.042881  14.601  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259639   0.006801  38.177  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.923620   0.042426  68.912  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.577779   0.025548  22.615  < 2e-16 ***
MG_250K_PLUS                         1.092778   0.071361  15.313  < 2e-16 ***
Alumnus                             -0.568595   0.021860 -26.011  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271593   0.060934  -4.457 8.35e-06 ***
AFFINITY_SCORE                       0.155950   0.006841  22.796  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123396   0.022218  -5.554 2.83e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.193508   0.028528   6.783 1.21e-11 ***
MG_PR_MODEL_DESCTop Tier             0.577802   0.028381  20.359  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9644 on 18756 degrees of freedom
Multiple R-squared:  0.7247,    Adjusted R-squared:  0.7244 
F-statistic:  2351 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1028 -0.6258 -0.1788  0.4967  6.3397 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.078849   0.114797   0.687  0.49218    
ns(NUMERIC_AGE, df = s)1             0.549310   0.102926   5.337 9.56e-08 ***
ns(NUMERIC_AGE, df = s)2             0.649338   0.130328   4.982 6.34e-07 ***
ns(NUMERIC_AGE, df = s)3             0.486487   0.114948   4.232 2.32e-05 ***
ns(NUMERIC_AGE, df = s)4             0.509428   0.123351   4.130 3.65e-05 ***
ns(NUMERIC_AGE, df = s)5             0.203694   0.120602   1.689  0.09124 .  
ns(NUMERIC_AGE, df = s)6            -0.090804   0.090411  -1.004  0.31522    
ns(NUMERIC_AGE, df = s)7             0.809044   0.258494   3.130  0.00175 ** 
ns(NUMERIC_AGE, df = s)8            -0.679541   0.147024  -4.622 3.83e-06 ***
PM_VISIT_LAST_2_YRS                  0.339835   0.029080  11.686  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.278136   0.029125   9.550  < 2e-16 ***
AF_25K_GIFT                          0.607513   0.043039  14.116  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258800   0.006806  38.024  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.915493   0.042638  68.378  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558145   0.025740  21.684  < 2e-16 ***
MG_250K_PLUS                         1.098893   0.071850  15.294  < 2e-16 ***
Alumnus                             -0.568868   0.021958 -25.907  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.286670   0.061928  -4.629 3.70e-06 ***
AFFINITY_SCORE                       0.158911   0.006857  23.176  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.115917   0.022296  -5.199 2.02e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.220942   0.028519   7.747 9.87e-15 ***
MG_PR_MODEL_DESCTop Tier             0.595249   0.028410  20.952  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9677 on 18756 degrees of freedom
Multiple R-squared:  0.7233,    Adjusted R-squared:  0.723 
F-statistic:  2335 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4449 -0.6200 -0.1781  0.4932  6.3541 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.033703   0.112716   0.299  0.76493    
ns(NUMERIC_AGE, df = s)1             0.614633   0.101090   6.080 1.22e-09 ***
ns(NUMERIC_AGE, df = s)2             0.746538   0.128159   5.825 5.80e-09 ***
ns(NUMERIC_AGE, df = s)3             0.554053   0.112870   4.909 9.24e-07 ***
ns(NUMERIC_AGE, df = s)4             0.550651   0.121208   4.543 5.58e-06 ***
ns(NUMERIC_AGE, df = s)5             0.274541   0.118571   2.315  0.02060 *  
ns(NUMERIC_AGE, df = s)6            -0.088302   0.089561  -0.986  0.32417    
ns(NUMERIC_AGE, df = s)7             0.935473   0.253931   3.684  0.00023 ***
ns(NUMERIC_AGE, df = s)8            -0.670750   0.146112  -4.591 4.45e-06 ***
PM_VISIT_LAST_2_YRS                  0.285080   0.029121   9.790  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301734   0.028875  10.450  < 2e-16 ***
AF_25K_GIFT                          0.614958   0.043462  14.149  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260416   0.006792  38.342  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912006   0.042483  68.545  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.561857   0.025558  21.984  < 2e-16 ***
MG_250K_PLUS                         1.141667   0.072210  15.810  < 2e-16 ***
Alumnus                             -0.577741   0.021762 -26.548  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.263108   0.060923  -4.319 1.58e-05 ***
AFFINITY_SCORE                       0.158678   0.006831  23.228  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131489   0.022191  -5.925 3.17e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.192268   0.028434   6.762 1.40e-11 ***
MG_PR_MODEL_DESCTop Tier             0.567385   0.028282  20.062  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.962 on 18756 degrees of freedom
Multiple R-squared:  0.7258,    Adjusted R-squared:  0.7255 
F-statistic:  2364 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4059 -0.6252 -0.1812  0.4954  6.3254 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.036317   0.112275   0.323 0.746349    
ns(NUMERIC_AGE, df = s)1             0.560646   0.100559   5.575 2.51e-08 ***
ns(NUMERIC_AGE, df = s)2             0.717072   0.127574   5.621 1.93e-08 ***
ns(NUMERIC_AGE, df = s)3             0.500916   0.112321   4.460 8.26e-06 ***
ns(NUMERIC_AGE, df = s)4             0.553400   0.120852   4.579 4.70e-06 ***
ns(NUMERIC_AGE, df = s)5             0.234639   0.118137   1.986 0.047030 *  
ns(NUMERIC_AGE, df = s)6            -0.141585   0.089535  -1.581 0.113816    
ns(NUMERIC_AGE, df = s)7             0.937660   0.253042   3.706 0.000212 ***
ns(NUMERIC_AGE, df = s)8            -0.596599   0.148405  -4.020 5.84e-05 ***
PM_VISIT_LAST_2_YRS                  0.307266   0.028926  10.623  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299547   0.028885  10.370  < 2e-16 ***
AF_25K_GIFT                          0.623457   0.042910  14.530  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263117   0.006788  38.759  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.898624   0.042475  68.243  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567138   0.025604  22.150  < 2e-16 ***
MG_250K_PLUS                         1.095351   0.072576  15.093  < 2e-16 ***
Alumnus                             -0.569093   0.021847 -26.049  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.262369   0.062160  -4.221 2.44e-05 ***
AFFINITY_SCORE                       0.157861   0.006834  23.099  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108504   0.022208  -4.886 1.04e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.213360   0.028467   7.495 6.92e-14 ***
MG_PR_MODEL_DESCTop Tier             0.580478   0.028328  20.491  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9632 on 18756 degrees of freedom
Multiple R-squared:  0.7251,    Adjusted R-squared:  0.7248 
F-statistic:  2356 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4564 -0.6278 -0.1766  0.4946  6.3215 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.021564   0.114573   0.188 0.850711    
ns(NUMERIC_AGE, df = s)1             0.607043   0.102690   5.911 3.45e-09 ***
ns(NUMERIC_AGE, df = s)2             0.707415   0.130058   5.439 5.42e-08 ***
ns(NUMERIC_AGE, df = s)3             0.537645   0.114753   4.685 2.82e-06 ***
ns(NUMERIC_AGE, df = s)4             0.590018   0.123170   4.790 1.68e-06 ***
ns(NUMERIC_AGE, df = s)5             0.260681   0.120440   2.164 0.030446 *  
ns(NUMERIC_AGE, df = s)6            -0.076845   0.090046  -0.853 0.393446    
ns(NUMERIC_AGE, df = s)7             0.940034   0.257719   3.648 0.000266 ***
ns(NUMERIC_AGE, df = s)8            -0.623593   0.144438  -4.317 1.59e-05 ***
PM_VISIT_LAST_2_YRS                  0.319424   0.029177  10.948  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287294   0.029090   9.876  < 2e-16 ***
AF_25K_GIFT                          0.577241   0.043671  13.218  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258946   0.006840  37.857  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899435   0.042572  68.107  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555049   0.025772  21.537  < 2e-16 ***
MG_250K_PLUS                         1.129104   0.072280  15.621  < 2e-16 ***
Alumnus                             -0.563961   0.021985 -25.652  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259871   0.061509  -4.225 2.40e-05 ***
AFFINITY_SCORE                       0.160015   0.006865  23.307  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116747   0.022253  -5.246 1.57e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200708   0.028546   7.031 2.12e-12 ***
MG_PR_MODEL_DESCTop Tier             0.586574   0.028416  20.643  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9678 on 18756 degrees of freedom
Multiple R-squared:  0.7221,    Adjusted R-squared:  0.7218 
F-statistic:  2321 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3971 -0.6232 -0.1815  0.4909  6.3536 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.027123   0.113380   0.239 0.810939    
ns(NUMERIC_AGE, df = s)1             0.597621   0.101547   5.885 4.04e-09 ***
ns(NUMERIC_AGE, df = s)2             0.673127   0.129078   5.215 1.86e-07 ***
ns(NUMERIC_AGE, df = s)3             0.528538   0.113797   4.645 3.43e-06 ***
ns(NUMERIC_AGE, df = s)4             0.557168   0.121192   4.597 4.31e-06 ***
ns(NUMERIC_AGE, df = s)5             0.252020   0.119091   2.116 0.034342 *  
ns(NUMERIC_AGE, df = s)6            -0.056571   0.090606  -0.624 0.532399    
ns(NUMERIC_AGE, df = s)7             0.874135   0.254922   3.429 0.000607 ***
ns(NUMERIC_AGE, df = s)8            -0.667931   0.144264  -4.630 3.68e-06 ***
PM_VISIT_LAST_2_YRS                  0.325656   0.028812  11.303  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276726   0.028919   9.569  < 2e-16 ***
AF_25K_GIFT                          0.570566   0.043052  13.253  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260012   0.006814  38.158  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911048   0.042689  68.192  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.575136   0.025625  22.444  < 2e-16 ***
MG_250K_PLUS                         1.074767   0.071610  15.009  < 2e-16 ***
Alumnus                             -0.564220   0.021862 -25.808  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.305317   0.061188  -4.990 6.10e-07 ***
AFFINITY_SCORE                       0.160432   0.006829  23.494  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118532   0.022292  -5.317 1.07e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.208829   0.028638   7.292 3.17e-13 ***
MG_PR_MODEL_DESCTop Tier             0.589972   0.028306  20.843  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.965 on 18756 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7254 
F-statistic:  2363 on 21 and 18756 DF,  p-value: < 2.2e-16


[[8]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3523 -0.6296 -0.1818  0.4960  6.3432 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         -0.008138   0.112603  -0.072   0.9424    
ns(NUMERIC_AGE, df = s)1             0.609698   0.100947   6.040 1.57e-09 ***
ns(NUMERIC_AGE, df = s)2             0.743336   0.127997   5.807 6.45e-09 ***
ns(NUMERIC_AGE, df = s)3             0.561059   0.112700   4.978 6.47e-07 ***
ns(NUMERIC_AGE, df = s)4             0.603178   0.121048   4.983 6.32e-07 ***
ns(NUMERIC_AGE, df = s)5             0.297353   0.118473   2.510   0.0121 *  
ns(NUMERIC_AGE, df = s)6            -0.061400   0.089399  -0.687   0.4922    
ns(NUMERIC_AGE, df = s)7             1.007888   0.253450   3.977 7.01e-05 ***
ns(NUMERIC_AGE, df = s)8            -0.663869   0.144642  -4.590 4.47e-06 ***
PM_VISIT_LAST_2_YRS                  0.312375   0.028896  10.810  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.284032   0.029045   9.779  < 2e-16 ***
AF_25K_GIFT                          0.613419   0.042912  14.295  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255119   0.006818  37.419  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917870   0.042654  68.407  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582135   0.025683  22.666  < 2e-16 ***
MG_250K_PLUS                         1.063860   0.071922  14.792  < 2e-16 ***
Alumnus                             -0.563594   0.021929 -25.700  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.295825   0.061903  -4.779 1.78e-06 ***
AFFINITY_SCORE                       0.161381   0.006842  23.585  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111513   0.022345  -4.990 6.08e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.204314   0.028612   7.141 9.61e-13 ***
MG_PR_MODEL_DESCTop Tier             0.579105   0.028410  20.384  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.967 on 18752 degrees of freedom
Multiple R-squared:  0.7235,    Adjusted R-squared:  0.7232 
F-statistic:  2336 on 21 and 18752 DF,  p-value: < 2.2e-16



[[9]]
[[9]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4335 -0.6243 -0.1774  0.4905  5.4701 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.043233   0.116657   0.371  0.71094    
ns(NUMERIC_AGE, df = s)1             0.609864   0.104591   5.831 5.60e-09 ***
ns(NUMERIC_AGE, df = s)2             0.659995   0.136859   4.822 1.43e-06 ***
ns(NUMERIC_AGE, df = s)3             0.569488   0.122633   4.644 3.44e-06 ***
ns(NUMERIC_AGE, df = s)4             0.508538   0.117941   4.312 1.63e-05 ***
ns(NUMERIC_AGE, df = s)5             0.533451   0.123794   4.309 1.65e-05 ***
ns(NUMERIC_AGE, df = s)6             0.232999   0.123264   1.890  0.05874 .  
ns(NUMERIC_AGE, df = s)7            -0.064007   0.091556  -0.699  0.48450    
ns(NUMERIC_AGE, df = s)8             0.804702   0.262519   3.065  0.00218 ** 
ns(NUMERIC_AGE, df = s)9            -0.757331   0.150135  -5.044 4.59e-07 ***
PM_VISIT_LAST_2_YRS                  0.312065   0.028729  10.862  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274254   0.028872   9.499  < 2e-16 ***
AF_25K_GIFT                          0.616623   0.042554  14.490  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260929   0.006782  38.472  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929151   0.042386  69.107  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558113   0.025528  21.863  < 2e-16 ***
MG_250K_PLUS                         1.127590   0.071596  15.749  < 2e-16 ***
Alumnus                             -0.570097   0.021959 -25.962  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.266193   0.061191  -4.350 1.37e-05 ***
AFFINITY_SCORE                       0.158088   0.006818  23.186  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117472   0.022124  -5.310 1.11e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.215304   0.028344   7.596 3.19e-14 ***
MG_PR_MODEL_DESCTop Tier             0.596040   0.028194  21.140  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9607 on 18755 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.727 
F-statistic:  2274 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4783 -0.6220 -0.1736  0.4908  6.3405 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.046815   0.118156   0.396  0.69195    
ns(NUMERIC_AGE, df = s)1             0.623539   0.106234   5.869 4.45e-09 ***
ns(NUMERIC_AGE, df = s)2             0.614043   0.136613   4.495 7.01e-06 ***
ns(NUMERIC_AGE, df = s)3             0.624711   0.124631   5.013 5.42e-07 ***
ns(NUMERIC_AGE, df = s)4             0.505469   0.118920   4.250 2.14e-05 ***
ns(NUMERIC_AGE, df = s)5             0.507807   0.125355   4.051 5.12e-05 ***
ns(NUMERIC_AGE, df = s)6             0.237740   0.124735   1.906  0.05667 .  
ns(NUMERIC_AGE, df = s)7            -0.081224   0.090229  -0.900  0.36803    
ns(NUMERIC_AGE, df = s)8             0.857762   0.264549   3.242  0.00119 ** 
ns(NUMERIC_AGE, df = s)9            -0.629725   0.135338  -4.653 3.29e-06 ***
PM_VISIT_LAST_2_YRS                  0.302704   0.028783  10.517  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279857   0.028952   9.666  < 2e-16 ***
AF_25K_GIFT                          0.588696   0.043186  13.632  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259856   0.006769  38.387  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912615   0.042399  68.695  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557303   0.025569  21.796  < 2e-16 ***
MG_250K_PLUS                         1.167038   0.070031  16.665  < 2e-16 ***
Alumnus                             -0.564550   0.022016 -25.643  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.258320   0.061793  -4.180 2.92e-05 ***
AFFINITY_SCORE                       0.158626   0.006815  23.275  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.122681   0.022103  -5.550 2.89e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208785   0.028342   7.367 1.82e-13 ***
MG_PR_MODEL_DESCTop Tier             0.601293   0.028341  21.217  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9606 on 18755 degrees of freedom
Multiple R-squared:  0.7276,    Adjusted R-squared:  0.7273 
F-statistic:  2277 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4010 -0.6236 -0.1796  0.4930  6.3656 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.109297   0.122011   0.896 0.370374    
ns(NUMERIC_AGE, df = s)1             0.559844   0.109655   5.106 3.33e-07 ***
ns(NUMERIC_AGE, df = s)2             0.555481   0.140669   3.949 7.88e-05 ***
ns(NUMERIC_AGE, df = s)3             0.554946   0.128305   4.325 1.53e-05 ***
ns(NUMERIC_AGE, df = s)4             0.428033   0.122885   3.483 0.000497 ***
ns(NUMERIC_AGE, df = s)5             0.447244   0.128947   3.468 0.000525 ***
ns(NUMERIC_AGE, df = s)6             0.155074   0.128345   1.208 0.226963    
ns(NUMERIC_AGE, df = s)7            -0.132127   0.093101  -1.419 0.155863    
ns(NUMERIC_AGE, df = s)8             0.698768   0.274223   2.548 0.010837 *  
ns(NUMERIC_AGE, df = s)9            -0.676936   0.149575  -4.526 6.06e-06 ***
PM_VISIT_LAST_2_YRS                  0.320899   0.028789  11.147  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263377   0.028884   9.118  < 2e-16 ***
AF_25K_GIFT                          0.613935   0.042749  14.361  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262919   0.006795  38.691  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.915772   0.042501  68.605  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563544   0.025669  21.954  < 2e-16 ***
MG_250K_PLUS                         1.090876   0.071105  15.342  < 2e-16 ***
Alumnus                             -0.573009   0.022006 -26.039  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.277043   0.060622  -4.570 4.91e-06 ***
AFFINITY_SCORE                       0.160243   0.006845  23.409  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110136   0.022172  -4.967 6.85e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207916   0.028456   7.307 2.85e-13 ***
MG_PR_MODEL_DESCTop Tier             0.595628   0.028348  21.011  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18755 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7259 
F-statistic:  2261 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4047 -0.6254 -0.1797  0.4936  6.3255 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.038428   0.118634   0.324 0.746004    
ns(NUMERIC_AGE, df = s)1             0.639086   0.106217   6.017 1.81e-09 ***
ns(NUMERIC_AGE, df = s)2             0.648075   0.137655   4.708 2.52e-06 ***
ns(NUMERIC_AGE, df = s)3             0.630982   0.124249   5.078 3.84e-07 ***
ns(NUMERIC_AGE, df = s)4             0.519313   0.119884   4.332 1.49e-05 ***
ns(NUMERIC_AGE, df = s)5             0.546987   0.125670   4.353 1.35e-05 ***
ns(NUMERIC_AGE, df = s)6             0.250869   0.125188   2.004 0.045090 *  
ns(NUMERIC_AGE, df = s)7            -0.091087   0.092550  -0.984 0.325036    
ns(NUMERIC_AGE, df = s)8             0.930163   0.266476   3.491 0.000483 ***
ns(NUMERIC_AGE, df = s)9            -0.592980   0.151347  -3.918 8.96e-05 ***
PM_VISIT_LAST_2_YRS                  0.320340   0.028951  11.065  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288493   0.028918   9.976  < 2e-16 ***
AF_25K_GIFT                          0.625901   0.042883  14.596  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259445   0.006803  38.136  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.925387   0.042457  68.903  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.578563   0.025560  22.636  < 2e-16 ***
MG_250K_PLUS                         1.093101   0.071362  15.318  < 2e-16 ***
Alumnus                             -0.571320   0.022021 -25.944  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271253   0.060934  -4.452 8.57e-06 ***
AFFINITY_SCORE                       0.155581   0.006850  22.713  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123681   0.022219  -5.566 2.63e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.193725   0.028529   6.791 1.15e-11 ***
MG_PR_MODEL_DESCTop Tier             0.577613   0.028382  20.351  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9644 on 18755 degrees of freedom
Multiple R-squared:  0.7247,    Adjusted R-squared:  0.7244 
F-statistic:  2244 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0945 -0.6265 -0.1794  0.4966  6.3468 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.080066   0.120305   0.666  0.50572    
ns(NUMERIC_AGE, df = s)1             0.589559   0.107668   5.476 4.41e-08 ***
ns(NUMERIC_AGE, df = s)2             0.606487   0.139588   4.345 1.40e-05 ***
ns(NUMERIC_AGE, df = s)3             0.566900   0.125829   4.505 6.67e-06 ***
ns(NUMERIC_AGE, df = s)4             0.474504   0.121674   3.900 9.66e-05 ***
ns(NUMERIC_AGE, df = s)5             0.504526   0.127373   3.961 7.49e-05 ***
ns(NUMERIC_AGE, df = s)6             0.182344   0.126832   1.438  0.15054    
ns(NUMERIC_AGE, df = s)7            -0.058585   0.093305  -0.628  0.53009    
ns(NUMERIC_AGE, df = s)8             0.769905   0.270527   2.846  0.00443 ** 
ns(NUMERIC_AGE, df = s)9            -0.702851   0.153113  -4.590 4.45e-06 ***
PM_VISIT_LAST_2_YRS                  0.339893   0.029081  11.688  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277844   0.029127   9.539  < 2e-16 ***
AF_25K_GIFT                          0.607484   0.043040  14.114  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258648   0.006809  37.988  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917173   0.042674  68.359  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558778   0.025752  21.699  < 2e-16 ***
MG_250K_PLUS                         1.099080   0.071854  15.296  < 2e-16 ***
Alumnus                             -0.571095   0.022123 -25.814  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.285957   0.061928  -4.618 3.91e-06 ***
AFFINITY_SCORE                       0.158578   0.006867  23.092  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116192   0.022297  -5.211 1.90e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221126   0.028521   7.753 9.42e-15 ***
MG_PR_MODEL_DESCTop Tier             0.595143   0.028411  20.948  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9678 on 18755 degrees of freedom
Multiple R-squared:  0.7233,    Adjusted R-squared:  0.723 
F-statistic:  2228 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4434 -0.6203 -0.1780  0.4929  6.3594 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.022080   0.118074   0.187 0.851661    
ns(NUMERIC_AGE, df = s)1             0.659209   0.105666   6.239 4.51e-10 ***
ns(NUMERIC_AGE, df = s)2             0.726015   0.137284   5.288 1.25e-07 ***
ns(NUMERIC_AGE, df = s)3             0.629364   0.123736   5.086 3.69e-07 ***
ns(NUMERIC_AGE, df = s)4             0.550476   0.119410   4.610 4.05e-06 ***
ns(NUMERIC_AGE, df = s)5             0.549707   0.125132   4.393 1.12e-05 ***
ns(NUMERIC_AGE, df = s)6             0.268880   0.124656   2.157 0.031020 *  
ns(NUMERIC_AGE, df = s)7            -0.058236   0.092449  -0.630 0.528751    
ns(NUMERIC_AGE, df = s)8             0.925741   0.265669   3.485 0.000494 ***
ns(NUMERIC_AGE, df = s)9            -0.683288   0.152317  -4.486 7.30e-06 ***
PM_VISIT_LAST_2_YRS                  0.284848   0.029122   9.781  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301671   0.028877  10.447  < 2e-16 ***
AF_25K_GIFT                          0.614871   0.043465  14.147  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260323   0.006794  38.315  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912918   0.042524  68.501  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.562144   0.025570  21.985  < 2e-16 ***
MG_250K_PLUS                         1.142265   0.072215  15.818  < 2e-16 ***
Alumnus                             -0.578818   0.021937 -26.386  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.262557   0.060923  -4.310 1.64e-05 ***
AFFINITY_SCORE                       0.158529   0.006842  23.171  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131673   0.022192  -5.933 3.02e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.192290   0.028436   6.762 1.40e-11 ***
MG_PR_MODEL_DESCTop Tier             0.567190   0.028283  20.054  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.962 on 18755 degrees of freedom
Multiple R-squared:  0.7258,    Adjusted R-squared:  0.7255 
F-statistic:  2256 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4047 -0.6251 -0.1818  0.4961  6.3309 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.021401   0.118198   0.181 0.856320    
ns(NUMERIC_AGE, df = s)1             0.621346   0.106059   5.858 4.75e-09 ***
ns(NUMERIC_AGE, df = s)2             0.685163   0.136634   5.015 5.36e-07 ***
ns(NUMERIC_AGE, df = s)3             0.606448   0.124526   4.870 1.12e-06 ***
ns(NUMERIC_AGE, df = s)4             0.518609   0.118872   4.363 1.29e-05 ***
ns(NUMERIC_AGE, df = s)5             0.554099   0.125323   4.421 9.86e-06 ***
ns(NUMERIC_AGE, df = s)6             0.228227   0.124679   1.831 0.067188 .  
ns(NUMERIC_AGE, df = s)7            -0.106242   0.092407  -1.150 0.250272    
ns(NUMERIC_AGE, df = s)8             0.938025   0.266728   3.517 0.000438 ***
ns(NUMERIC_AGE, df = s)9            -0.610726   0.154684  -3.948 7.90e-05 ***
PM_VISIT_LAST_2_YRS                  0.306956   0.028928  10.611  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299472   0.028887  10.367  < 2e-16 ***
AF_25K_GIFT                          0.623342   0.042913  14.526  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263047   0.006791  38.736  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899187   0.042517  68.188  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567433   0.025620  22.148  < 2e-16 ***
MG_250K_PLUS                         1.095916   0.072585  15.098  < 2e-16 ***
Alumnus                             -0.569906   0.022009 -25.894  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.261744   0.062161  -4.211 2.56e-05 ***
AFFINITY_SCORE                       0.157738   0.006846  23.042  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108838   0.022210  -4.900 9.65e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.213298   0.028469   7.492 7.07e-14 ***
MG_PR_MODEL_DESCTop Tier             0.580252   0.028330  20.482  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9632 on 18755 degrees of freedom
Multiple R-squared:  0.7251,    Adjusted R-squared:  0.7248 
F-statistic:  2249 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4515 -0.6272 -0.1762  0.4941  6.3301 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.031027   0.120668   0.257  0.79708    
ns(NUMERIC_AGE, df = s)1             0.657012   0.108424   6.060 1.39e-09 ***
ns(NUMERIC_AGE, df = s)2             0.639457   0.139258   4.592 4.42e-06 ***
ns(NUMERIC_AGE, df = s)3             0.645665   0.127182   5.077 3.88e-07 ***
ns(NUMERIC_AGE, df = s)4             0.524711   0.121506   4.318 1.58e-05 ***
ns(NUMERIC_AGE, df = s)5             0.585682   0.127770   4.584 4.59e-06 ***
ns(NUMERIC_AGE, df = s)6             0.224496   0.127126   1.766  0.07742 .  
ns(NUMERIC_AGE, df = s)7            -0.037044   0.092985  -0.398  0.69035    
ns(NUMERIC_AGE, df = s)8             0.877655   0.271614   3.231  0.00123 ** 
ns(NUMERIC_AGE, df = s)9            -0.654601   0.150392  -4.353 1.35e-05 ***
PM_VISIT_LAST_2_YRS                  0.319466   0.029177  10.949  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287100   0.029091   9.869  < 2e-16 ***
AF_25K_GIFT                          0.576832   0.043673  13.208  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258713   0.006843  37.810  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901738   0.042615  68.092  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555890   0.025784  21.560  < 2e-16 ***
MG_250K_PLUS                         1.129077   0.072282  15.620  < 2e-16 ***
Alumnus                             -0.566801   0.022143 -25.597  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.259179   0.061506  -4.214 2.52e-05 ***
AFFINITY_SCORE                       0.159587   0.006875  23.212  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117186   0.022253  -5.266 1.41e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200778   0.028547   7.033 2.09e-12 ***
MG_PR_MODEL_DESCTop Tier             0.586304   0.028415  20.633  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9678 on 18755 degrees of freedom
Multiple R-squared:  0.7222,    Adjusted R-squared:  0.7218 
F-statistic:  2216 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3947 -0.6230 -0.1812  0.4884  6.3617 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.029109   0.119431   0.244  0.80744    
ns(NUMERIC_AGE, df = s)1             0.645416   0.107136   6.024 1.73e-09 ***
ns(NUMERIC_AGE, df = s)2             0.630709   0.138170   4.565 5.03e-06 ***
ns(NUMERIC_AGE, df = s)3             0.608862   0.125883   4.837 1.33e-06 ***
ns(NUMERIC_AGE, df = s)4             0.523269   0.120154   4.355 1.34e-05 ***
ns(NUMERIC_AGE, df = s)5             0.533972   0.126523   4.220 2.45e-05 ***
ns(NUMERIC_AGE, df = s)6             0.226269   0.125820   1.798  0.07214 .  
ns(NUMERIC_AGE, df = s)7            -0.031000   0.092771  -0.334  0.73826    
ns(NUMERIC_AGE, df = s)8             0.827702   0.268929   3.078  0.00209 ** 
ns(NUMERIC_AGE, df = s)9            -0.692540   0.151941  -4.558 5.20e-06 ***
PM_VISIT_LAST_2_YRS                  0.325604   0.028814  11.300  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276539   0.028921   9.562  < 2e-16 ***
AF_25K_GIFT                          0.570474   0.043055  13.250  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259873   0.006817  38.122  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912117   0.042721  68.167  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.575610   0.025640  22.449  < 2e-16 ***
MG_250K_PLUS                         1.075213   0.071617  15.013  < 2e-16 ***
Alumnus                             -0.565649   0.022023 -25.684  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.304681   0.061188  -4.979 6.44e-07 ***
AFFINITY_SCORE                       0.160207   0.006839  23.427  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118849   0.022294  -5.331 9.88e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208923   0.028639   7.295 3.11e-13 ***
MG_PR_MODEL_DESCTop Tier             0.589796   0.028307  20.836  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9651 on 18755 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7254 
F-statistic:  2256 on 22 and 18755 DF,  p-value: < 2.2e-16


[[9]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3512 -0.6298 -0.1829  0.4973  6.3486 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         -0.024470   0.118491  -0.207  0.83639    
ns(NUMERIC_AGE, df = s)1             0.671216   0.106448   6.306 2.94e-10 ***
ns(NUMERIC_AGE, df = s)2             0.722151   0.137164   5.265 1.42e-07 ***
ns(NUMERIC_AGE, df = s)3             0.649514   0.125062   5.194 2.08e-07 ***
ns(NUMERIC_AGE, df = s)4             0.579473   0.119211   4.861 1.18e-06 ***
ns(NUMERIC_AGE, df = s)5             0.605008   0.125475   4.822 1.43e-06 ***
ns(NUMERIC_AGE, df = s)6             0.294908   0.124928   2.361  0.01825 *  
ns(NUMERIC_AGE, df = s)7            -0.027618   0.092272  -0.299  0.76471    
ns(NUMERIC_AGE, df = s)8             1.007782   0.266916   3.776  0.00016 ***
ns(NUMERIC_AGE, df = s)9            -0.678070   0.150608  -4.502 6.76e-06 ***
PM_VISIT_LAST_2_YRS                  0.312174   0.028898  10.803  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.284044   0.029046   9.779  < 2e-16 ***
AF_25K_GIFT                          0.613257   0.042915  14.290  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255034   0.006821  37.391  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.918396   0.042687  68.368  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582313   0.025695  22.662  < 2e-16 ***
MG_250K_PLUS                         1.064423   0.071930  14.798  < 2e-16 ***
Alumnus                             -0.564290   0.022098 -25.536  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.295481   0.061904  -4.773 1.83e-06 ***
AFFINITY_SCORE                       0.161297   0.006852  23.541  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111683   0.022347  -4.998 5.85e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.204245   0.028614   7.138 9.82e-13 ***
MG_PR_MODEL_DESCTop Tier             0.578858   0.028411  20.374  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.967 on 18751 degrees of freedom
Multiple R-squared:  0.7235,    Adjusted R-squared:  0.7232 
F-statistic:  2230 on 22 and 18751 DF,  p-value: < 2.2e-16



[[10]]
[[10]][[1]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4338 -0.6232 -0.1776  0.4907  5.4696 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.045625   0.122081   0.374 0.708609    
ns(NUMERIC_AGE, df = s)1             0.654440   0.110407   5.927 3.13e-09 ***
ns(NUMERIC_AGE, df = s)2             0.614194   0.139090   4.416 1.01e-05 ***
ns(NUMERIC_AGE, df = s)3             0.633304   0.128248   4.938 7.96e-07 ***
ns(NUMERIC_AGE, df = s)4             0.529191   0.127971   4.135 3.56e-05 ***
ns(NUMERIC_AGE, df = s)5             0.501455   0.127868   3.922 8.83e-05 ***
ns(NUMERIC_AGE, df = s)6             0.488045   0.133022   3.669 0.000244 ***
ns(NUMERIC_AGE, df = s)7             0.214799   0.128283   1.674 0.094066 .  
ns(NUMERIC_AGE, df = s)8            -0.053982   0.094517  -0.571 0.567915    
ns(NUMERIC_AGE, df = s)9             0.749495   0.275334   2.722 0.006492 ** 
ns(NUMERIC_AGE, df = s)10           -0.767586   0.158471  -4.844 1.28e-06 ***
PM_VISIT_LAST_2_YRS                  0.311947   0.028737  10.855  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.274306   0.028874   9.500  < 2e-16 ***
AF_25K_GIFT                          0.617249   0.042558  14.504  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260948   0.006782  38.475  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.929075   0.042379  69.116  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557944   0.025526  21.858  < 2e-16 ***
MG_250K_PLUS                         1.127215   0.071603  15.743  < 2e-16 ***
Alumnus                             -0.569195   0.021887 -26.006  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.265905   0.061191  -4.346 1.40e-05 ***
AFFINITY_SCORE                       0.158130   0.006816  23.199  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117494   0.022125  -5.310 1.11e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.215385   0.028345   7.599 3.13e-14 ***
MG_PR_MODEL_DESCTop Tier             0.596052   0.028195  21.140  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9607 on 18754 degrees of freedom
Multiple R-squared:  0.7273,    Adjusted R-squared:  0.727 
F-statistic:  2175 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[2]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4781 -0.6215 -0.1742  0.4902  6.3395 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.065092   0.120479   0.540  0.58901    
ns(NUMERIC_AGE, df = s)1             0.641767   0.108831   5.897 3.77e-09 ***
ns(NUMERIC_AGE, df = s)2             0.555837   0.137488   4.043 5.30e-05 ***
ns(NUMERIC_AGE, df = s)3             0.656240   0.126805   5.175 2.30e-07 ***
ns(NUMERIC_AGE, df = s)4             0.505317   0.126532   3.994 6.53e-05 ***
ns(NUMERIC_AGE, df = s)5             0.501402   0.126368   3.968 7.28e-05 ***
ns(NUMERIC_AGE, df = s)6             0.426201   0.131794   3.234  0.00122 ** 
ns(NUMERIC_AGE, df = s)7             0.218974   0.126854   1.726  0.08433 .  
ns(NUMERIC_AGE, df = s)8            -0.099782   0.092097  -1.083  0.27862    
ns(NUMERIC_AGE, df = s)9             0.787760   0.270396   2.913  0.00358 ** 
ns(NUMERIC_AGE, df = s)10           -0.619381   0.142122  -4.358 1.32e-05 ***
PM_VISIT_LAST_2_YRS                  0.302919   0.028789  10.522  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.279885   0.028953   9.667  < 2e-16 ***
AF_25K_GIFT                          0.589053   0.043186  13.640  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259871   0.006769  38.392  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912623   0.042390  68.710  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.557267   0.025565  21.798  < 2e-16 ***
MG_250K_PLUS                         1.166267   0.070037  16.652  < 2e-16 ***
Alumnus                             -0.564245   0.021954 -25.701  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.258138   0.061794  -4.177 2.96e-05 ***
AFFINITY_SCORE                       0.158633   0.006813  23.285  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.122624   0.022106  -5.547 2.94e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208872   0.028343   7.370 1.78e-13 ***
MG_PR_MODEL_DESCTop Tier             0.601409   0.028341  21.220  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9606 on 18754 degrees of freedom
Multiple R-squared:  0.7276,    Adjusted R-squared:  0.7273 
F-statistic:  2178 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[3]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4021 -0.6237 -0.1802  0.4930  6.3664 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.119698   0.124563   0.961 0.336594    
ns(NUMERIC_AGE, df = s)1             0.577046   0.112454   5.131 2.90e-07 ***
ns(NUMERIC_AGE, df = s)2             0.517996   0.141867   3.651 0.000262 ***
ns(NUMERIC_AGE, df = s)3             0.572305   0.130679   4.379 1.20e-05 ***
ns(NUMERIC_AGE, df = s)4             0.459434   0.130582   3.518 0.000435 ***
ns(NUMERIC_AGE, df = s)5             0.405757   0.130265   3.115 0.001843 ** 
ns(NUMERIC_AGE, df = s)6             0.389882   0.135504   2.877 0.004016 ** 
ns(NUMERIC_AGE, df = s)7             0.132434   0.130660   1.014 0.310798    
ns(NUMERIC_AGE, df = s)8            -0.137503   0.095122  -1.446 0.148323    
ns(NUMERIC_AGE, df = s)9             0.645110   0.280645   2.299 0.021535 *  
ns(NUMERIC_AGE, df = s)10           -0.681722   0.157370  -4.332 1.49e-05 ***
PM_VISIT_LAST_2_YRS                  0.320753   0.028798  11.138  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.263349   0.028886   9.117  < 2e-16 ***
AF_25K_GIFT                          0.614628   0.042750  14.377  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.262958   0.006795  38.698  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.915344   0.042494  68.605  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.563173   0.025666  21.943  < 2e-16 ***
MG_250K_PLUS                         1.090582   0.071111  15.336  < 2e-16 ***
Alumnus                             -0.571488   0.021946 -26.040  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.276817   0.060624  -4.566 5.00e-06 ***
AFFINITY_SCORE                       0.160404   0.006843  23.442  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.110160   0.022175  -4.968 6.83e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.207948   0.028457   7.307 2.83e-13 ***
MG_PR_MODEL_DESCTop Tier             0.595617   0.028350  21.010  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9637 on 18754 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7259 
F-statistic:  2163 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[4]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4075 -0.6248 -0.1795  0.4937  6.3254 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.037467   0.121563   0.308 0.757924    
ns(NUMERIC_AGE, df = s)1             0.671386   0.109812   6.114 9.91e-10 ***
ns(NUMERIC_AGE, df = s)2             0.629826   0.138650   4.543 5.59e-06 ***
ns(NUMERIC_AGE, df = s)3             0.650725   0.127889   5.088 3.65e-07 ***
ns(NUMERIC_AGE, df = s)4             0.573117   0.127570   4.493 7.08e-06 ***
ns(NUMERIC_AGE, df = s)5             0.500060   0.127306   3.928 8.60e-05 ***
ns(NUMERIC_AGE, df = s)6             0.500646   0.132678   3.773 0.000162 ***
ns(NUMERIC_AGE, df = s)7             0.238443   0.127813   1.866 0.062118 .  
ns(NUMERIC_AGE, df = s)8            -0.093824   0.094576  -0.992 0.321188    
ns(NUMERIC_AGE, df = s)9             0.902970   0.274344   3.291 0.000999 ***
ns(NUMERIC_AGE, df = s)10           -0.591629   0.159489  -3.710 0.000208 ***
PM_VISIT_LAST_2_YRS                  0.319836   0.028959  11.045  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.288517   0.028920   9.976  < 2e-16 ***
AF_25K_GIFT                          0.626357   0.042886  14.605  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259593   0.006803  38.160  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.924222   0.042453  68.881  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.577937   0.025558  22.612  < 2e-16 ***
MG_250K_PLUS                         1.093082   0.071374  15.315  < 2e-16 ***
Alumnus                             -0.568680   0.021961 -25.895  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271128   0.060937  -4.449 8.66e-06 ***
AFFINITY_SCORE                       0.155868   0.006849  22.759  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.123690   0.022221  -5.566 2.64e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.193754   0.028530   6.791 1.15e-11 ***
MG_PR_MODEL_DESCTop Tier             0.577720   0.028384  20.354  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9644 on 18754 degrees of freedom
Multiple R-squared:  0.7247,    Adjusted R-squared:  0.7244 
F-statistic:  2146 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[5]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0884 -0.6274 -0.1803  0.4969  6.3486 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.087512   0.123370   0.709 0.478118    
ns(NUMERIC_AGE, df = s)1             0.616635   0.111417   5.534 3.16e-08 ***
ns(NUMERIC_AGE, df = s)2             0.569451   0.140751   4.046 5.24e-05 ***
ns(NUMERIC_AGE, df = s)3             0.602023   0.129630   4.644 3.44e-06 ***
ns(NUMERIC_AGE, df = s)4             0.502361   0.129488   3.880 0.000105 ***
ns(NUMERIC_AGE, df = s)5             0.460388   0.129285   3.561 0.000370 ***
ns(NUMERIC_AGE, df = s)6             0.441658   0.134451   3.285 0.001022 ** 
ns(NUMERIC_AGE, df = s)7             0.166120   0.129611   1.282 0.199969    
ns(NUMERIC_AGE, df = s)8            -0.063914   0.095323  -0.670 0.502551    
ns(NUMERIC_AGE, df = s)9             0.719871   0.278743   2.583 0.009814 ** 
ns(NUMERIC_AGE, df = s)10           -0.711998   0.161236  -4.416 1.01e-05 ***
PM_VISIT_LAST_2_YRS                  0.339703   0.029087  11.679  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.277797   0.029130   9.537  < 2e-16 ***
AF_25K_GIFT                          0.608039   0.043043  14.126  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258764   0.006808  38.009  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.916552   0.042668  68.355  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.558397   0.025751  21.685  < 2e-16 ***
MG_250K_PLUS                         1.098807   0.071863  15.290  < 2e-16 ***
Alumnus                             -0.569354   0.022062 -25.807  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.285810   0.061930  -4.615 3.96e-06 ***
AFFINITY_SCORE                       0.158742   0.006865  23.123  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.116212   0.022300  -5.211 1.89e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.221227   0.028522   7.756 9.19e-15 ***
MG_PR_MODEL_DESCTop Tier             0.595238   0.028412  20.950  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9678 on 18754 degrees of freedom
Multiple R-squared:  0.7233,    Adjusted R-squared:  0.7229 
F-statistic:  2131 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[6]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4442 -0.6201 -0.1804  0.4929  6.3621 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.026568   0.121043   0.219 0.826273    
ns(NUMERIC_AGE, df = s)1             0.690341   0.109284   6.317 2.73e-10 ***
ns(NUMERIC_AGE, df = s)2             0.684220   0.138333   4.946 7.63e-07 ***
ns(NUMERIC_AGE, df = s)3             0.685649   0.127415   5.381 7.49e-08 ***
ns(NUMERIC_AGE, df = s)4             0.586344   0.127087   4.614 3.98e-06 ***
ns(NUMERIC_AGE, df = s)5             0.526686   0.126893   4.151 3.33e-05 ***
ns(NUMERIC_AGE, df = s)6             0.504157   0.132143   3.815 0.000136 ***
ns(NUMERIC_AGE, df = s)7             0.250931   0.127334   1.971 0.048779 *  
ns(NUMERIC_AGE, df = s)8            -0.059144   0.094519  -0.626 0.531498    
ns(NUMERIC_AGE, df = s)9             0.883715   0.273672   3.229 0.001244 ** 
ns(NUMERIC_AGE, df = s)10           -0.694774   0.160614  -4.326 1.53e-05 ***
PM_VISIT_LAST_2_YRS                  0.284618   0.029130   9.771  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.301587   0.028878  10.443  < 2e-16 ***
AF_25K_GIFT                          0.615356   0.043467  14.157  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.260368   0.006794  38.325  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.912634   0.042514  68.510  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.561952   0.025569  21.978  < 2e-16 ***
MG_250K_PLUS                         1.142162   0.072224  15.814  < 2e-16 ***
Alumnus                             -0.577556   0.021868 -26.412  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.262046   0.060924  -4.301 1.71e-05 ***
AFFINITY_SCORE                       0.158630   0.006840  23.193  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.131752   0.022195  -5.936 2.97e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.192383   0.028438   6.765 1.37e-11 ***
MG_PR_MODEL_DESCTop Tier             0.567270   0.028285  20.056  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9621 on 18754 degrees of freedom
Multiple R-squared:  0.7258,    Adjusted R-squared:  0.7254 
F-statistic:  2158 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[7]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4050 -0.6253 -0.1823  0.4948  6.3325 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.027658   0.120547   0.229 0.818531    
ns(NUMERIC_AGE, df = s)1             0.645226   0.108676   5.937 2.95e-09 ***
ns(NUMERIC_AGE, df = s)2             0.640236   0.137537   4.655 3.26e-06 ***
ns(NUMERIC_AGE, df = s)3             0.657866   0.126826   5.187 2.16e-07 ***
ns(NUMERIC_AGE, df = s)4             0.530040   0.126578   4.187 2.83e-05 ***
ns(NUMERIC_AGE, df = s)5             0.517861   0.126416   4.096 4.21e-05 ***
ns(NUMERIC_AGE, df = s)6             0.491601   0.131849   3.729 0.000193 ***
ns(NUMERIC_AGE, df = s)7             0.209767   0.126854   1.654 0.098224 .  
ns(NUMERIC_AGE, df = s)8            -0.106291   0.094396  -1.126 0.260174    
ns(NUMERIC_AGE, df = s)9             0.895662   0.272846   3.283 0.001030 ** 
ns(NUMERIC_AGE, df = s)10           -0.616409   0.163155  -3.778 0.000159 ***
PM_VISIT_LAST_2_YRS                  0.306975   0.028935  10.609  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.299437   0.028889  10.365  < 2e-16 ***
AF_25K_GIFT                          0.623662   0.042915  14.532  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.263054   0.006790  38.739  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.899153   0.042511  68.198  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.567405   0.025617  22.149  < 2e-16 ***
MG_250K_PLUS                         1.095521   0.072595  15.091  < 2e-16 ***
Alumnus                             -0.569044   0.021947 -25.928  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.261525   0.062164  -4.207 2.60e-05 ***
AFFINITY_SCORE                       0.157780   0.006843  23.056  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.108744   0.022214  -4.895 9.89e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.213414   0.028471   7.496 6.88e-14 ***
MG_PR_MODEL_DESCTop Tier             0.580368   0.028332  20.484  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9633 on 18754 degrees of freedom
Multiple R-squared:  0.7251,    Adjusted R-squared:  0.7248 
F-statistic:  2151 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[8]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4532 -0.6294 -0.1756  0.4950  6.3330 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.037361   0.123113   0.303   0.7615    
ns(NUMERIC_AGE, df = s)1             0.675219   0.111148   6.075 1.26e-09 ***
ns(NUMERIC_AGE, df = s)2             0.612503   0.140335   4.365 1.28e-05 ***
ns(NUMERIC_AGE, df = s)3             0.658857   0.129439   5.090 3.61e-07 ***
ns(NUMERIC_AGE, df = s)4             0.554129   0.129296   4.286 1.83e-05 ***
ns(NUMERIC_AGE, df = s)5             0.513087   0.129042   3.976 7.03e-05 ***
ns(NUMERIC_AGE, df = s)6             0.526015   0.134296   3.917 9.00e-05 ***
ns(NUMERIC_AGE, df = s)7             0.202513   0.129352   1.566   0.1175    
ns(NUMERIC_AGE, df = s)8            -0.034019   0.094975  -0.358   0.7202    
ns(NUMERIC_AGE, df = s)9             0.827350   0.277806   2.978   0.0029 ** 
ns(NUMERIC_AGE, df = s)10           -0.671168   0.158346  -4.239 2.26e-05 ***
PM_VISIT_LAST_2_YRS                  0.319250   0.029183  10.940  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.287108   0.029093   9.869  < 2e-16 ***
AF_25K_GIFT                          0.577569   0.043674  13.224  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.258790   0.006842  37.823  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.901147   0.042609  68.087  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.555450   0.025783  21.543  < 2e-16 ***
MG_250K_PLUS                         1.128778   0.072288  15.615  < 2e-16 ***
Alumnus                             -0.564653   0.022088 -25.564  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.258830   0.061510  -4.208 2.59e-05 ***
AFFINITY_SCORE                       0.159796   0.006873  23.249  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.117215   0.022256  -5.267 1.40e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.200730   0.028548   7.031 2.12e-12 ***
MG_PR_MODEL_DESCTop Tier             0.586308   0.028417  20.632  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9678 on 18754 degrees of freedom
Multiple R-squared:  0.7221,    Adjusted R-squared:  0.7218 
F-statistic:  2119 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[9]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3955 -0.6228 -0.1819  0.4895  6.3636 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.038515   0.121883   0.316 0.752009    
ns(NUMERIC_AGE, df = s)1             0.662175   0.109797   6.031 1.66e-09 ***
ns(NUMERIC_AGE, df = s)2             0.601090   0.139208   4.318 1.58e-05 ***
ns(NUMERIC_AGE, df = s)3             0.628427   0.128174   4.903 9.52e-07 ***
ns(NUMERIC_AGE, df = s)4             0.539485   0.127915   4.218 2.48e-05 ***
ns(NUMERIC_AGE, df = s)5             0.509153   0.127714   3.987 6.73e-05 ***
ns(NUMERIC_AGE, df = s)6             0.469417   0.133108   3.527 0.000422 ***
ns(NUMERIC_AGE, df = s)7             0.209926   0.128019   1.640 0.101064    
ns(NUMERIC_AGE, df = s)8            -0.037962   0.094769  -0.401 0.688738    
ns(NUMERIC_AGE, df = s)9             0.774043   0.275217   2.812 0.004921 ** 
ns(NUMERIC_AGE, df = s)10           -0.700509   0.159991  -4.378 1.20e-05 ***
PM_VISIT_LAST_2_YRS                  0.325519   0.028820  11.295  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.276466   0.028923   9.559  < 2e-16 ***
AF_25K_GIFT                          0.570900   0.043057  13.259  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259924   0.006816  38.134  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.911847   0.042717  68.165  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.575435   0.025637  22.445  < 2e-16 ***
MG_250K_PLUS                         1.074981   0.071624  15.009  < 2e-16 ***
Alumnus                             -0.564507   0.021966 -25.699  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.304305   0.061190  -4.973 6.65e-07 ***
AFFINITY_SCORE                       0.160304   0.006837  23.448  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118835   0.022296  -5.330 9.94e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.208983   0.028641   7.297 3.07e-13 ***
MG_PR_MODEL_DESCTop Tier             0.589832   0.028309  20.836  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9651 on 18754 degrees of freedom
Multiple R-squared:  0.7257,    Adjusted R-squared:  0.7254 
F-statistic:  2158 on 23 and 18754 DF,  p-value: < 2.2e-16


[[10]][[10]]

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = s) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3524 -0.6300 -0.1813  0.4976  6.3522 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         -0.020997   0.120858  -0.174 0.862079    
ns(NUMERIC_AGE, df = s)1             0.695485   0.109060   6.377 1.85e-10 ***
ns(NUMERIC_AGE, df = s)2             0.689322   0.137927   4.998 5.85e-07 ***
ns(NUMERIC_AGE, df = s)3             0.684533   0.126741   5.401 6.71e-08 ***
ns(NUMERIC_AGE, df = s)4             0.600084   0.124075   4.836 1.33e-06 ***
ns(NUMERIC_AGE, df = s)5             0.558748   0.129208   4.324 1.54e-05 ***
ns(NUMERIC_AGE, df = s)6             0.543331   0.132323   4.106 4.04e-05 ***
ns(NUMERIC_AGE, df = s)7             0.277227   0.126814   2.186 0.028821 *  
ns(NUMERIC_AGE, df = s)8            -0.023697   0.095017  -0.249 0.803053    
ns(NUMERIC_AGE, df = s)9             0.965485   0.272955   3.537 0.000405 ***
ns(NUMERIC_AGE, df = s)10           -0.695447   0.159381  -4.363 1.29e-05 ***
PM_VISIT_LAST_2_YRS                  0.311908   0.028905  10.791  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.284065   0.029048   9.779  < 2e-16 ***
AF_25K_GIFT                          0.613754   0.042917  14.301  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.255098   0.006820  37.404  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917974   0.042682  68.365  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.582031   0.025696  22.651  < 2e-16 ***
MG_250K_PLUS                         1.064365   0.071938  14.796  < 2e-16 ***
Alumnus                             -0.562790   0.022052 -25.521  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.295258   0.061907  -4.769 1.86e-06 ***
AFFINITY_SCORE                       0.161418   0.006851  23.560  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.111784   0.022350  -5.002 5.74e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.204190   0.028616   7.136 9.99e-13 ***
MG_PR_MODEL_DESCTop Tier             0.578817   0.028414  20.371  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9671 on 18750 degrees of freedom
Multiple R-squared:  0.7235,    Adjusted R-squared:  0.7231 
F-statistic:  2133 on 23 and 18750 DF,  p-value: < 2.2e-16

Campaign final models

Back

summary(clm_final)

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = mdat %>% 
    filter(rownum %in% unlist(xval_inds)))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8516 -1.2287 -0.1483  0.9862  5.6918 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.34421    0.02665  50.430  < 2e-16 ***
ACTIVE_PROPOSALS           0.28318    0.04351   6.509 7.75e-11 ***
AGE                       -0.22153    0.02035 -10.886  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.61254    0.04596  13.329  < 2e-16 ***
VISITS_5PLUS               0.74935    0.03304  22.677  < 2e-16 ***
AF_25K_GIFT                0.83750    0.05752  14.561  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.04013    0.02217  92.019  < 2e-16 ***
MG_250K_PLUS               1.10852    0.09647  11.491  < 2e-16 ***
PRESIDENT_VISIT            0.45473    0.08038   5.657 1.56e-08 ***
TRUSTEE_OR_ADVISORY_BOARD  0.27001    0.04310   6.265 3.81e-10 ***
Alumnus                   -0.11548    0.02628  -4.395 1.11e-05 ***
DEEP_ENGAGEMENT            0.33019    0.02181  15.137  < 2e-16 ***
CHICAGO_HOME               0.09710    0.02119   4.582 4.63e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 20851 degrees of freedom
Multiple R-squared:  0.4654,    Adjusted R-squared:  0.4651 
F-statistic:  1512 on 12 and 20851 DF,  p-value: < 2.2e-16

summary(clmap_final)

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = mdat %>% 
    filter(rownum %in% unlist(xval_inds)))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4145 -0.6252 -0.1852  0.4953  6.2824 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.273983   0.089046   3.077  0.00209 ** 
ns(NUMERIC_AGE, df = 5)1             0.372461   0.081347   4.579 4.71e-06 ***
ns(NUMERIC_AGE, df = 5)2             0.236252   0.095335   2.478  0.01322 *  
ns(NUMERIC_AGE, df = 5)3            -0.385478   0.065826  -5.856 4.81e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.472451   0.201811   2.341  0.01924 *  
ns(NUMERIC_AGE, df = 5)5            -0.466161   0.108905  -4.280 1.87e-05 ***
PM_VISIT_LAST_2_YRS                  0.313757   0.027446  11.432  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.282839   0.027478  10.293  < 2e-16 ***
AF_25K_GIFT                          0.604838   0.040840  14.810  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.259760   0.006446  40.295  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.915133   0.040261  72.406  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.566151   0.024289  23.309  < 2e-16 ***
MG_250K_PLUS                         1.106584   0.067980  16.278  < 2e-16 ***
Alumnus                             -0.572008   0.020363 -28.091  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.272529   0.058270  -4.677 2.93e-06 ***
AFFINITY_SCORE                       0.158965   0.006473  24.557  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.118435   0.021085  -5.617 1.97e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.206051   0.027034   7.622 2.60e-14 ***
MG_PR_MODEL_DESCTop Tier             0.585706   0.026890  21.781  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9646 on 20845 degrees of freedom
Multiple R-squared:  0.7248,    Adjusted R-squared:  0.7246 
F-statistic:  3050 on 18 and 20845 DF,  p-value: < 2.2e-16

Transaction cultivation results

Back

lapply(tlms, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3186 -0.5623  0.1469  0.7486  3.8462 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.76745    0.02333  75.755  < 2e-16 ***
ACTIVE_PROPOSALS           0.23081    0.03785   6.098 1.10e-09 ***
AGE                        0.28694    0.01779  16.128  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.25410    0.04006   6.344 2.30e-10 ***
VISITS_5PLUS               0.73105    0.02894  25.258  < 2e-16 ***
AF_25K_GIFT                0.87487    0.04999  17.502  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.77836    0.01936  40.198  < 2e-16 ***
MG_250K_PLUS               1.78288    0.08481  21.023  < 2e-16 ***
PRESIDENT_VISIT            0.14916    0.07040   2.119   0.0341 *  
TRUSTEE_OR_ADVISORY_BOARD  0.34185    0.03747   9.123  < 2e-16 ***
Alumnus                    0.02082    0.02301   0.905   0.3657    
DEEP_ENGAGEMENT            0.18227    0.01910   9.542  < 2e-16 ***
CHICAGO_HOME               0.13213    0.01853   7.132 1.02e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.116 on 18765 degrees of freedom
Multiple R-squared:  0.3308,    Adjusted R-squared:  0.3304 
F-statistic: 773.1 on 12 and 18765 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3375 -0.5622  0.1450  0.7466  3.8217 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.75663    0.02338  75.147  < 2e-16 ***
ACTIVE_PROPOSALS           0.24415    0.03814   6.402 1.57e-10 ***
AGE                        0.28706    0.01783  16.102  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.25264    0.04025   6.276 3.54e-10 ***
VISITS_5PLUS               0.72456    0.02901  24.977  < 2e-16 ***
AF_25K_GIFT                0.82841    0.05085  16.292  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78491    0.01940  40.460  < 2e-16 ***
MG_250K_PLUS               1.79186    0.08278  21.645  < 2e-16 ***
PRESIDENT_VISIT            0.17113    0.07032   2.434    0.015 *  
TRUSTEE_OR_ADVISORY_BOARD  0.35174    0.03777   9.313  < 2e-16 ***
Alumnus                    0.02498    0.02306   1.083    0.279    
DEEP_ENGAGEMENT            0.18836    0.01908   9.872  < 2e-16 ***
CHICAGO_HOME               0.12592    0.01856   6.784 1.21e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.117 on 18765 degrees of freedom
Multiple R-squared:  0.3327,    Adjusted R-squared:  0.3323 
F-statistic: 779.8 on 12 and 18765 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3315 -0.5628  0.1504  0.7505  3.8532 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.75160    0.02334  75.043  < 2e-16 ***
ACTIVE_PROPOSALS           0.22904    0.03821   5.994 2.08e-09 ***
AGE                        0.28566    0.01789  15.970  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.27140    0.04037   6.723 1.83e-11 ***
VISITS_5PLUS               0.72201    0.02898  24.910  < 2e-16 ***
AF_25K_GIFT                0.85276    0.05024  16.973  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78434    0.01948  40.258  < 2e-16 ***
MG_250K_PLUS               1.75443    0.08418  20.841  < 2e-16 ***
PRESIDENT_VISIT            0.15976    0.07009   2.279   0.0227 *  
TRUSTEE_OR_ADVISORY_BOARD  0.36026    0.03794   9.495  < 2e-16 ***
Alumnus                    0.02844    0.02303   1.235   0.2167    
DEEP_ENGAGEMENT            0.19772    0.01912  10.340  < 2e-16 ***
CHICAGO_HOME               0.12891    0.01864   6.915 4.82e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.12 on 18765 degrees of freedom
Multiple R-squared:  0.3324,    Adjusted R-squared:  0.332 
F-statistic: 778.8 on 12 and 18765 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3606 -0.5623  0.1493  0.7406  3.8029 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               1.778531   0.023248  76.502  < 2e-16 ***
ACTIVE_PROPOSALS          0.214159   0.038120   5.618 1.96e-08 ***
AGE                       0.284538   0.017781  16.002  < 2e-16 ***
PM_VISIT_LAST_2_YRS       0.271383   0.040322   6.730 1.74e-11 ***
VISITS_5PLUS              0.717755   0.028830  24.896  < 2e-16 ***
AF_25K_GIFT               0.878243   0.050065  17.542  < 2e-16 ***
GAVE_IN_LAST_3_YRS        0.779877   0.019349  40.305  < 2e-16 ***
MG_250K_PLUS              1.720435   0.084170  20.440  < 2e-16 ***
PRESIDENT_VISIT           0.198072   0.070362   2.815  0.00488 ** 
TRUSTEE_OR_ADVISORY_BOARD 0.364242   0.037852   9.623  < 2e-16 ***
Alumnus                   0.008213   0.022931   0.358  0.72022    
DEEP_ENGAGEMENT           0.180692   0.019013   9.504  < 2e-16 ***
CHICAGO_HOME              0.128785   0.018510   6.958 3.57e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.115 on 18765 degrees of freedom
Multiple R-squared:  0.3309,    Adjusted R-squared:  0.3305 
F-statistic: 773.3 on 12 and 18765 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3484 -0.5621  0.1438  0.7444  3.8409 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.76082    0.02340  75.241  < 2e-16 ***
ACTIVE_PROPOSALS           0.22065    0.03842   5.744 9.41e-09 ***
AGE                        0.27916    0.01787  15.621  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.27569    0.04043   6.820 9.40e-12 ***
VISITS_5PLUS               0.72535    0.02915  24.886  < 2e-16 ***
AF_25K_GIFT                0.87277    0.05036  17.332  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78059    0.01948  40.077  < 2e-16 ***
MG_250K_PLUS               1.72772    0.08436  20.481  < 2e-16 ***
PRESIDENT_VISIT            0.17132    0.06995   2.449   0.0143 *  
TRUSTEE_OR_ADVISORY_BOARD  0.35803    0.03773   9.490  < 2e-16 ***
Alumnus                    0.02431    0.02308   1.053   0.2923    
DEEP_ENGAGEMENT            0.19459    0.01917  10.152  < 2e-16 ***
CHICAGO_HOME               0.12749    0.01859   6.858 7.22e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.119 on 18765 degrees of freedom
Multiple R-squared:  0.3294,    Adjusted R-squared:  0.329 
F-statistic: 768.2 on 12 and 18765 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3704 -0.5681  0.1468  0.7489  3.7989 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.76913    0.02337  75.697  < 2e-16 ***
ACTIVE_PROPOSALS           0.22722    0.03831   5.931 3.06e-09 ***
AGE                        0.28119    0.01784  15.763  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.26562    0.04057   6.547 6.02e-11 ***
VISITS_5PLUS               0.72319    0.02900  24.939  < 2e-16 ***
AF_25K_GIFT                0.84522    0.05116  16.522  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.79322    0.01946  40.755  < 2e-16 ***
MG_250K_PLUS               1.76585    0.08572  20.599  < 2e-16 ***
PRESIDENT_VISIT            0.19841    0.07114   2.789  0.00529 ** 
TRUSTEE_OR_ADVISORY_BOARD  0.35323    0.03775   9.356  < 2e-16 ***
Alumnus                    0.01008    0.02300   0.438  0.66114    
DEEP_ENGAGEMENT            0.18683    0.01912   9.770  < 2e-16 ***
CHICAGO_HOME               0.13176    0.01855   7.101 1.28e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.119 on 18765 degrees of freedom
Multiple R-squared:  0.3287,    Adjusted R-squared:  0.3283 
F-statistic: 765.9 on 12 and 18765 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2998 -0.5609  0.1453  0.7529  3.7220 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.75324    0.02349  74.653  < 2e-16 ***
ACTIVE_PROPOSALS           0.18994    0.03832   4.957 7.22e-07 ***
AGE                        0.28183    0.01787  15.769  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.27612    0.04031   6.850 7.61e-12 ***
VISITS_5PLUS               0.72514    0.02896  25.039  < 2e-16 ***
AF_25K_GIFT                0.87361    0.05028  17.373  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78174    0.01950  40.095  < 2e-16 ***
MG_250K_PLUS               1.80905    0.08569  21.112  < 2e-16 ***
PRESIDENT_VISIT            0.21417    0.07052   3.037  0.00239 ** 
TRUSTEE_OR_ADVISORY_BOARD  0.36563    0.03760   9.723  < 2e-16 ***
Alumnus                    0.03023    0.02311   1.308  0.19086    
DEEP_ENGAGEMENT            0.18909    0.01923   9.835  < 2e-16 ***
CHICAGO_HOME               0.13609    0.01859   7.319 2.60e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.12 on 18765 degrees of freedom
Multiple R-squared:  0.3282,    Adjusted R-squared:  0.3278 
F-statistic: 764.1 on 12 and 18765 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3759 -0.5612  0.1379  0.7464  3.8046 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.75493    0.02335  75.166  < 2e-16 ***
ACTIVE_PROPOSALS           0.22829    0.03810   5.992 2.12e-09 ***
AGE                        0.29702    0.01781  16.681  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.27159    0.04042   6.719 1.88e-11 ***
VISITS_5PLUS               0.72938    0.02894  25.204  < 2e-16 ***
AF_25K_GIFT                0.84439    0.05070  16.653  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78387    0.01941  40.395  < 2e-16 ***
MG_250K_PLUS               1.76296    0.08481  20.787  < 2e-16 ***
PRESIDENT_VISIT            0.20579    0.07095   2.901  0.00373 ** 
TRUSTEE_OR_ADVISORY_BOARD  0.34503    0.03814   9.046  < 2e-16 ***
Alumnus                    0.02668    0.02302   1.159  0.24644    
DEEP_ENGAGEMENT            0.18590    0.01908   9.740  < 2e-16 ***
CHICAGO_HOME               0.13296    0.01858   7.158 8.49e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.116 on 18765 degrees of freedom
Multiple R-squared:  0.3305,    Adjusted R-squared:  0.3301 
F-statistic:   772 on 12 and 18765 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3025 -0.5698  0.1384  0.7503  3.8497 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.76942    0.02332  75.860  < 2e-16 ***
ACTIVE_PROPOSALS           0.22474    0.03779   5.947 2.78e-09 ***
AGE                        0.28791    0.01787  16.116  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.25554    0.03994   6.398 1.62e-10 ***
VISITS_5PLUS               0.72293    0.02900  24.930  < 2e-16 ***
AF_25K_GIFT                0.84878    0.05042  16.833  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78918    0.01947  40.543  < 2e-16 ***
MG_250K_PLUS               1.75295    0.08456  20.730  < 2e-16 ***
PRESIDENT_VISIT            0.14977    0.07013   2.136   0.0327 *  
TRUSTEE_OR_ADVISORY_BOARD  0.35549    0.03774   9.420  < 2e-16 ***
Alumnus                    0.01547    0.02299   0.673   0.5009    
DEEP_ENGAGEMENT            0.18007    0.01914   9.408  < 2e-16 ***
CHICAGO_HOME               0.13009    0.01857   7.006 2.53e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.118 on 18765 degrees of freedom
Multiple R-squared:  0.331, Adjusted R-squared:  0.3306 
F-statistic: 773.6 on 12 and 18765 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3104 -0.5563  0.1455  0.7466  3.8405 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.75738    0.02334  75.294  < 2e-16 ***
ACTIVE_PROPOSALS           0.24164    0.03801   6.356 2.11e-10 ***
AGE                        0.29278    0.01779  16.459  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.24898    0.04006   6.215 5.24e-10 ***
VISITS_5PLUS               0.71995    0.02876  25.029  < 2e-16 ***
AF_25K_GIFT                0.88025    0.05003  17.595  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.77776    0.01937  40.146  < 2e-16 ***
MG_250K_PLUS               1.75820    0.08443  20.824  < 2e-16 ***
PRESIDENT_VISIT            0.15414    0.07058   2.184    0.029 *  
TRUSTEE_OR_ADVISORY_BOARD  0.35971    0.03770   9.541  < 2e-16 ***
Alumnus                    0.02544    0.02303   1.105    0.269    
DEEP_ENGAGEMENT            0.18433    0.01908   9.660  < 2e-16 ***
CHICAGO_HOME               0.13271    0.01859   7.140 9.65e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.116 on 18761 degrees of freedom
Multiple R-squared:  0.3313,    Adjusted R-squared:  0.3308 
F-statistic: 774.5 on 12 and 18761 DF,  p-value: < 2.2e-16

Transaction all predictors 1

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lapply(tlmaps, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7053 -0.6568 -0.0170  0.5699  4.3328 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.021955   0.101441  10.074  < 2e-16 ***
ACTIVE_PROPOSALS                     0.064916   0.029946   2.168 0.030190 *  
ns(NUMERIC_AGE, df = 5)1             0.184741   0.078192   2.363 0.018155 *  
ns(NUMERIC_AGE, df = 5)2             0.020354   0.091488   0.222 0.823946    
ns(NUMERIC_AGE, df = 5)3            -0.296885   0.062743  -4.732 2.24e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.370226   0.193198   1.916 0.055341 .  
ns(NUMERIC_AGE, df = 5)5            -0.066365   0.103325  -0.642 0.520690    
PM_VISIT_LAST_2_YRS                  0.126677   0.032031   3.955 7.69e-05 ***
log10plus1(VISIT_COUNT)              0.482156   0.027181  17.739  < 2e-16 ***
AF_25K_GIFT                          0.631661   0.039377  16.041  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.440404   0.006149  71.620  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.413020   0.038425  10.749  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.538985   0.023122 -23.311  < 2e-16 ***
MG_250K_PLUS                         1.415328   0.070100  20.190  < 2e-16 ***
PRESIDENT_VISIT                      0.054140   0.056502   0.958 0.337977    
TRUSTEE_OR_ADVISORY_BOARD            0.092482   0.029565   3.128 0.001762 ** 
Alumnus                             -0.651132   0.021967 -29.642  < 2e-16 ***
DOUBLE_ALUM                          0.026994   0.020840   1.295 0.195231    
EVER_PARENT                         -0.079988   0.019923  -4.015 5.97e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.268744   0.056051  -4.795 1.64e-06 ***
CHICAGO_HOME                         0.052070   0.015205   3.425 0.000617 ***
QUAL_LEVELA1 $100M+                  1.215117   0.440200   2.760 0.005779 ** 
QUAL_LEVELA2 $50M - 99.9M            0.899936   0.393330   2.288 0.022149 *  
QUAL_LEVELA3 $25M - $49.9M           0.058131   0.196288   0.296 0.767118    
QUAL_LEVELA4 $10M - $24.9M           0.528339   0.135672   3.894 9.88e-05 ***
QUAL_LEVELA5 $5M - $9.9M             0.524364   0.102983   5.092 3.58e-07 ***
QUAL_LEVELA6 $2M - $4.9M             0.417957   0.093096   4.490 7.18e-06 ***
QUAL_LEVELA7 $1M - $1.9M             0.351987   0.073720   4.775 1.81e-06 ***
QUAL_LEVELB  $500K - $999K           0.177756   0.066557   2.671 0.007575 ** 
QUAL_LEVELC  $250K - $499K           0.093235   0.063005   1.480 0.138941    
QUAL_LEVELD  $100K - $249K           0.036398   0.062122   0.586 0.557939    
QUAL_LEVELE  $50K - $99K             0.076065   0.063542   1.197 0.231292    
QUAL_LEVELF  $25K - $49K             0.072746   0.063740   1.141 0.253763    
QUAL_LEVELG  $10K - $24K            -0.027438   0.063047  -0.435 0.663428    
QUAL_LEVELH  Under $10K              0.011597   0.202922   0.057 0.954426    
QUAL_LEVELJ  Future Prospect        -0.574098   0.616611  -0.931 0.351838    
AFFINITY_SCORE                       0.082169   0.006523  12.596  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.106857   0.020745   5.151 2.62e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.107075   0.026045   4.111 3.95e-05 ***
MG_PR_MODEL_DESCTop Tier             0.325651   0.026368  12.350  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.867 on 18738 degrees of freedom
Multiple R-squared:  0.5967,    Adjusted R-squared:  0.5958 
F-statistic: 710.8 on 39 and 18738 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1745 -0.6505 -0.0165  0.5678  4.2902 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.066102   0.100013  10.660  < 2e-16 ***
ACTIVE_PROPOSALS                     0.079937   0.030049   2.660 0.007815 ** 
ns(NUMERIC_AGE, df = 5)1             0.171675   0.077135   2.226 0.026050 *  
ns(NUMERIC_AGE, df = 5)2             0.022380   0.090368   0.248 0.804406    
ns(NUMERIC_AGE, df = 5)3            -0.321323   0.061160  -5.254 1.51e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.371307   0.189527   1.959 0.050113 .  
ns(NUMERIC_AGE, df = 5)5            -0.049503   0.093959  -0.527 0.598295    
PM_VISIT_LAST_2_YRS                  0.118800   0.032053   3.706 0.000211 ***
log10plus1(VISIT_COUNT)              0.455210   0.027168  16.756  < 2e-16 ***
AF_25K_GIFT                          0.590294   0.039856  14.811  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441899   0.006123  72.176  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.399268   0.038306  10.423  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.557109   0.023096 -24.122  < 2e-16 ***
MG_250K_PLUS                         1.406444   0.068493  20.534  < 2e-16 ***
PRESIDENT_VISIT                      0.047047   0.056337   0.835 0.403673    
TRUSTEE_OR_ADVISORY_BOARD            0.088780   0.029713   2.988 0.002812 ** 
Alumnus                             -0.649609   0.021897 -29.666  < 2e-16 ***
DOUBLE_ALUM                          0.020791   0.020657   1.006 0.314192    
EVER_PARENT                         -0.079549   0.019828  -4.012 6.04e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.253123   0.056386  -4.489 7.19e-06 ***
CHICAGO_HOME                         0.052021   0.015147   3.434 0.000595 ***
QUAL_LEVELA1 $100M+                  0.647735   0.392745   1.649 0.099113 .  
QUAL_LEVELA2 $50M - 99.9M            0.561881   0.358652   1.567 0.117215    
QUAL_LEVELA3 $25M - $49.9M           0.172196   0.196342   0.877 0.380486    
QUAL_LEVELA4 $10M - $24.9M           0.683036   0.139451   4.898 9.76e-07 ***
QUAL_LEVELA5 $5M - $9.9M             0.452690   0.100601   4.500 6.84e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.390070   0.092543   4.215 2.51e-05 ***
QUAL_LEVELA7 $1M - $1.9M             0.314154   0.073337   4.284 1.85e-05 ***
QUAL_LEVELB  $500K - $999K           0.114483   0.066024   1.734 0.082942 .  
QUAL_LEVELC  $250K - $499K           0.030799   0.062501   0.493 0.622175    
QUAL_LEVELD  $100K - $249K          -0.023645   0.061588  -0.384 0.701042    
QUAL_LEVELE  $50K - $99K             0.019218   0.063035   0.305 0.760463    
QUAL_LEVELF  $25K - $49K             0.020928   0.063238   0.331 0.740695    
QUAL_LEVELG  $10K - $24K            -0.075554   0.062495  -1.209 0.226699    
QUAL_LEVELH  Under $10K             -0.144356   0.212210  -0.680 0.496356    
QUAL_LEVELJ  Future Prospect        -0.637646   0.614744  -1.037 0.299630    
AFFINITY_SCORE                       0.085044   0.006504  13.076  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.114493   0.020685   5.535 3.15e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.132334   0.025974   5.095 3.52e-07 ***
MG_PR_MODEL_DESCTop Tier             0.361791   0.026449  13.679  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8644 on 18738 degrees of freedom
Multiple R-squared:  0.6007,    Adjusted R-squared:  0.5999 
F-statistic: 722.9 on 39 and 18738 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1278 -0.6566 -0.0156  0.5675  4.2994 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.086437   0.102040  10.647  < 2e-16 ***
ACTIVE_PROPOSALS                     0.064324   0.030158   2.133 0.032946 *  
ns(NUMERIC_AGE, df = 5)1             0.160076   0.078909   2.029 0.042512 *  
ns(NUMERIC_AGE, df = 5)2            -0.024792   0.092411  -0.268 0.788485    
ns(NUMERIC_AGE, df = 5)3            -0.302639   0.062854  -4.815 1.48e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.280442   0.195058   1.438 0.150525    
ns(NUMERIC_AGE, df = 5)5            -0.090808   0.102840  -0.883 0.377247    
PM_VISIT_LAST_2_YRS                  0.131969   0.032261   4.091 4.32e-05 ***
log10plus1(VISIT_COUNT)              0.473675   0.027180  17.427  < 2e-16 ***
AF_25K_GIFT                          0.619364   0.039519  15.672  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.443350   0.006152  72.068  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.391987   0.038436  10.198  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.553741   0.023220 -23.848  < 2e-16 ***
MG_250K_PLUS                         1.427278   0.069665  20.488  < 2e-16 ***
PRESIDENT_VISIT                      0.041928   0.056175   0.746 0.455446    
TRUSTEE_OR_ADVISORY_BOARD            0.096112   0.029844   3.220 0.001282 ** 
Alumnus                             -0.650304   0.021957 -29.618  < 2e-16 ***
DOUBLE_ALUM                          0.026954   0.020762   1.298 0.194214    
EVER_PARENT                         -0.068840   0.019945  -3.452 0.000559 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.268059   0.055506  -4.829 1.38e-06 ***
CHICAGO_HOME                         0.053142   0.015264   3.481 0.000500 ***
QUAL_LEVELA1 $100M+                  0.643544   0.394718   1.630 0.103036    
QUAL_LEVELA2 $50M - 99.9M            0.474391   0.439695   1.079 0.280642    
QUAL_LEVELA3 $25M - $49.9M           0.185759   0.213986   0.868 0.385356    
QUAL_LEVELA4 $10M - $24.9M           0.487373   0.136105   3.581 0.000343 ***
QUAL_LEVELA5 $5M - $9.9M             0.440485   0.101038   4.360 1.31e-05 ***
QUAL_LEVELA6 $2M - $4.9M             0.326500   0.092158   3.543 0.000397 ***
QUAL_LEVELA7 $1M - $1.9M             0.314727   0.074140   4.245 2.20e-05 ***
QUAL_LEVELB  $500K - $999K           0.111854   0.066415   1.684 0.092164 .  
QUAL_LEVELC  $250K - $499K           0.043985   0.062890   0.699 0.484313    
QUAL_LEVELD  $100K - $249K          -0.006485   0.061981  -0.105 0.916666    
QUAL_LEVELE  $50K - $99K             0.041382   0.063457   0.652 0.514324    
QUAL_LEVELF  $25K - $49K             0.019167   0.063613   0.301 0.763181    
QUAL_LEVELG  $10K - $24K            -0.082945   0.062915  -1.318 0.187393    
QUAL_LEVELH  Under $10K             -0.046328   0.203245  -0.228 0.819695    
QUAL_LEVELJ  Future Prospect        -0.628660   0.617795  -1.018 0.308887    
AFFINITY_SCORE                       0.086452   0.006532  13.235  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.111683   0.020772   5.377 7.68e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.109709   0.026130   4.199 2.70e-05 ***
MG_PR_MODEL_DESCTop Tier             0.333230   0.026504  12.573  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8687 on 18738 degrees of freedom
Multiple R-squared:  0.599, Adjusted R-squared:  0.5982 
F-statistic: 717.8 on 39 and 18738 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1385 -0.6477 -0.0173  0.5656  4.2500 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.075776   0.101189  10.631  < 2e-16 ***
ACTIVE_PROPOSALS                     0.056357   0.030121   1.871 0.061359 .  
ns(NUMERIC_AGE, df = 5)1             0.208437   0.077625   2.685 0.007256 ** 
ns(NUMERIC_AGE, df = 5)2             0.046877   0.090814   0.516 0.605731    
ns(NUMERIC_AGE, df = 5)3            -0.271445   0.062506  -4.343 1.41e-05 ***
ns(NUMERIC_AGE, df = 5)4             0.436106   0.191861   2.273 0.023036 *  
ns(NUMERIC_AGE, df = 5)5            -0.091444   0.103066  -0.887 0.374960    
PM_VISIT_LAST_2_YRS                  0.136709   0.032271   4.236 2.28e-05 ***
log10plus1(VISIT_COUNT)              0.466296   0.027101  17.206  < 2e-16 ***
AF_25K_GIFT                          0.638429   0.039410  16.200  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.437477   0.006140  71.250  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.417345   0.038257  10.909  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.542582   0.023043 -23.547  < 2e-16 ***
MG_250K_PLUS                         1.367698   0.069519  19.674  < 2e-16 ***
PRESIDENT_VISIT                      0.088922   0.056649   1.570 0.116503    
TRUSTEE_OR_ADVISORY_BOARD            0.106150   0.029852   3.556 0.000378 ***
Alumnus                             -0.662163   0.021901 -30.234  < 2e-16 ***
DOUBLE_ALUM                          0.023889   0.020738   1.152 0.249369    
EVER_PARENT                         -0.076855   0.019788  -3.884 0.000103 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.280754   0.055550  -5.054 4.37e-07 ***
CHICAGO_HOME                         0.054466   0.015196   3.584 0.000339 ***
QUAL_LEVELA1 $100M+                  0.598298   0.393617   1.520 0.128528    
QUAL_LEVELA2 $50M - 99.9M            0.545790   0.359455   1.518 0.128935    
QUAL_LEVELA3 $25M - $49.9M           0.051140   0.196861   0.260 0.795039    
QUAL_LEVELA4 $10M - $24.9M           0.518233   0.133633   3.878 0.000106 ***
QUAL_LEVELA5 $5M - $9.9M             0.447974   0.100738   4.447 8.76e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.312072   0.094260   3.311 0.000932 ***
QUAL_LEVELA7 $1M - $1.9M             0.283104   0.073930   3.829 0.000129 ***
QUAL_LEVELB  $500K - $999K           0.114047   0.066556   1.714 0.086627 .  
QUAL_LEVELC  $250K - $499K           0.022118   0.062963   0.351 0.725384    
QUAL_LEVELD  $100K - $249K          -0.026800   0.062075  -0.432 0.665934    
QUAL_LEVELE  $50K - $99K             0.011933   0.063540   0.188 0.851037    
QUAL_LEVELF  $25K - $49K             0.014054   0.063731   0.221 0.825464    
QUAL_LEVELG  $10K - $24K            -0.089411   0.063059  -1.418 0.156239    
QUAL_LEVELH  Under $10K             -0.094743   0.207598  -0.456 0.648122    
QUAL_LEVELJ  Future Prospect        -0.539164   0.868765  -0.621 0.534864    
AFFINITY_SCORE                       0.083320   0.006522  12.775  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.095045   0.020754   4.580 4.69e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.100137   0.026102   3.836 0.000125 ***
MG_PR_MODEL_DESCTop Tier             0.334006   0.026433  12.636  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8661 on 18738 degrees of freedom
Multiple R-squared:  0.5966,    Adjusted R-squared:  0.5957 
F-statistic: 710.5 on 39 and 18738 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1457 -0.6529 -0.0178  0.5671  4.3115 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.017443   0.101075  10.066  < 2e-16 ***
ACTIVE_PROPOSALS                     0.058092   0.030271   1.919 0.054987 .  
ns(NUMERIC_AGE, df = 5)1             0.195604   0.078173   2.502 0.012351 *  
ns(NUMERIC_AGE, df = 5)2             0.041959   0.091551   0.458 0.646734    
ns(NUMERIC_AGE, df = 5)3            -0.317950   0.062817  -5.062 4.20e-07 ***
ns(NUMERIC_AGE, df = 5)4             0.404442   0.193538   2.090 0.036656 *  
ns(NUMERIC_AGE, df = 5)5            -0.083635   0.104361  -0.801 0.422907    
PM_VISIT_LAST_2_YRS                  0.143388   0.032307   4.438 9.12e-06 ***
log10plus1(VISIT_COUNT)              0.469516   0.027281  17.210  < 2e-16 ***
AF_25K_GIFT                          0.635891   0.039555  16.076  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.443313   0.006132  72.298  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.402426   0.038376  10.486  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.549712   0.023167 -23.728  < 2e-16 ***
MG_250K_PLUS                         1.380573   0.069561  19.847  < 2e-16 ***
PRESIDENT_VISIT                      0.058229   0.056022   1.039 0.298633    
TRUSTEE_OR_ADVISORY_BOARD            0.094628   0.029678   3.189 0.001432 ** 
Alumnus                             -0.658483   0.021956 -29.991  < 2e-16 ***
DOUBLE_ALUM                          0.030263   0.020917   1.447 0.147966    
EVER_PARENT                         -0.076887   0.019963  -3.851 0.000118 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.280913   0.056326  -4.987 6.18e-07 ***
CHICAGO_HOME                         0.056806   0.015195   3.738 0.000186 ***
QUAL_LEVELA1 $100M+                  0.660152   0.394234   1.675 0.094046 .  
QUAL_LEVELA2 $50M - 99.9M            0.589679   0.360033   1.638 0.101471    
QUAL_LEVELA3 $25M - $49.9M           0.168707   0.209285   0.806 0.420189    
QUAL_LEVELA4 $10M - $24.9M           0.524055   0.134585   3.894 9.90e-05 ***
QUAL_LEVELA5 $5M - $9.9M             0.493427   0.101009   4.885 1.04e-06 ***
QUAL_LEVELA6 $2M - $4.9M             0.351755   0.091754   3.834 0.000127 ***
QUAL_LEVELA7 $1M - $1.9M             0.347161   0.074195   4.679 2.90e-06 ***
QUAL_LEVELB  $500K - $999K           0.150040   0.066751   2.248 0.024603 *  
QUAL_LEVELC  $250K - $499K           0.062887   0.063226   0.995 0.319924    
QUAL_LEVELD  $100K - $249K           0.007581   0.062327   0.122 0.903196    
QUAL_LEVELE  $50K - $99K             0.045734   0.063757   0.717 0.473190    
QUAL_LEVELF  $25K - $49K             0.041980   0.064004   0.656 0.511902    
QUAL_LEVELG  $10K - $24K            -0.061653   0.063198  -0.976 0.329295    
QUAL_LEVELH  Under $10K             -0.064750   0.208030  -0.311 0.755612    
QUAL_LEVELJ  Future Prospect        -0.616339   0.617007  -0.999 0.317848    
AFFINITY_SCORE                       0.086197   0.006514  13.233  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.119110   0.020762   5.737 9.80e-09 ***
MG_PR_MODEL_DESCMiddle Tier          0.110832   0.026037   4.257 2.08e-05 ***
MG_PR_MODEL_DESCTop Tier             0.334177   0.026393  12.662  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8675 on 18738 degrees of freedom
Multiple R-squared:  0.5976,    Adjusted R-squared:  0.5968 
F-statistic: 713.5 on 39 and 18738 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1443 -0.6546 -0.0173  0.5677  4.3298 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.014074   0.101487   9.992  < 2e-16 ***
ACTIVE_PROPOSALS                     0.077205   0.030203   2.556 0.010590 *  
ns(NUMERIC_AGE, df = 5)1             0.199207   0.077457   2.572 0.010124 *  
ns(NUMERIC_AGE, df = 5)2             0.018731   0.090691   0.207 0.836377    
ns(NUMERIC_AGE, df = 5)3            -0.298376   0.062630  -4.764 1.91e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.370236   0.191737   1.931 0.053503 .  
ns(NUMERIC_AGE, df = 5)5            -0.081337   0.103847  -0.783 0.433494    
PM_VISIT_LAST_2_YRS                  0.126247   0.032356   3.902 9.58e-05 ***
log10plus1(VISIT_COUNT)              0.471299   0.027197  17.329  < 2e-16 ***
AF_25K_GIFT                          0.621000   0.040161  15.463  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.444489   0.006157  72.187  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.410265   0.038479  10.662  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.545219   0.023137 -23.565  < 2e-16 ***
MG_250K_PLUS                         1.429444   0.070625  20.240  < 2e-16 ***
PRESIDENT_VISIT                      0.092413   0.057064   1.619 0.105368    
TRUSTEE_OR_ADVISORY_BOARD            0.092666   0.029721   3.118 0.001824 ** 
Alumnus                             -0.663096   0.021946 -30.215  < 2e-16 ***
DOUBLE_ALUM                          0.021844   0.020822   1.049 0.294142    
EVER_PARENT                         -0.079606   0.019927  -3.995 6.49e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.285269   0.055779  -5.114 3.18e-07 ***
CHICAGO_HOME                         0.056936   0.015197   3.746 0.000180 ***
QUAL_LEVELA1 $100M+                  0.558186   0.439163   1.271 0.203737    
QUAL_LEVELA2 $50M - 99.9M            0.607994   0.360193   1.688 0.091434 .  
QUAL_LEVELA3 $25M - $49.9M           0.118317   0.193583   0.611 0.541079    
QUAL_LEVELA4 $10M - $24.9M           0.531333   0.137850   3.854 0.000116 ***
QUAL_LEVELA5 $5M - $9.9M             0.548998   0.102961   5.332 9.82e-08 ***
QUAL_LEVELA6 $2M - $4.9M             0.367464   0.094332   3.895 9.84e-05 ***
QUAL_LEVELA7 $1M - $1.9M             0.361506   0.074969   4.822 1.43e-06 ***
QUAL_LEVELB  $500K - $999K           0.169760   0.067755   2.505 0.012237 *  
QUAL_LEVELC  $250K - $499K           0.096903   0.064182   1.510 0.131111    
QUAL_LEVELD  $100K - $249K           0.049975   0.063331   0.789 0.430055    
QUAL_LEVELE  $50K - $99K             0.075665   0.064760   1.168 0.242663    
QUAL_LEVELF  $25K - $49K             0.082905   0.064965   1.276 0.201921    
QUAL_LEVELG  $10K - $24K            -0.004286   0.064242  -0.067 0.946812    
QUAL_LEVELH  Under $10K             -0.023687   0.213432  -0.111 0.911634    
QUAL_LEVELJ  Future Prospect        -0.564666   0.617079  -0.915 0.360170    
AFFINITY_SCORE                       0.082990   0.006537  12.696  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.098747   0.020811   4.745 2.10e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.098389   0.026119   3.767 0.000166 ***
MG_PR_MODEL_DESCTop Tier             0.321191   0.026428  12.153  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8675 on 18738 degrees of freedom
Multiple R-squared:  0.5972,    Adjusted R-squared:  0.5963 
F-statistic: 712.2 on 39 and 18738 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0940 -0.6544 -0.0166  0.5659  4.2983 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.068654   0.100047  10.681  < 2e-16 ***
ACTIVE_PROPOSALS                     0.041140   0.030204   1.362 0.173194    
ns(NUMERIC_AGE, df = 5)1             0.163003   0.077090   2.114 0.034491 *  
ns(NUMERIC_AGE, df = 5)2             0.002642   0.090354   0.029 0.976669    
ns(NUMERIC_AGE, df = 5)3            -0.335122   0.062730  -5.342 9.29e-08 ***
ns(NUMERIC_AGE, df = 5)4             0.333280   0.191010   1.745 0.081030 .  
ns(NUMERIC_AGE, df = 5)5            -0.088111   0.105052  -0.839 0.401627    
PM_VISIT_LAST_2_YRS                  0.132220   0.032201   4.106 4.04e-05 ***
log10plus1(VISIT_COUNT)              0.477731   0.027194  17.567  < 2e-16 ***
AF_25K_GIFT                          0.637426   0.039471  16.149  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.444150   0.006142  72.308  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.401218   0.038443  10.437  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.541001   0.023160 -23.359  < 2e-16 ***
MG_250K_PLUS                         1.439520   0.070275  20.484  < 2e-16 ***
PRESIDENT_VISIT                      0.098560   0.056371   1.748 0.080407 .  
TRUSTEE_OR_ADVISORY_BOARD            0.098376   0.029591   3.324 0.000887 ***
Alumnus                             -0.655582   0.022033 -29.755  < 2e-16 ***
DOUBLE_ALUM                          0.025264   0.020952   1.206 0.227898    
EVER_PARENT                         -0.084963   0.020016  -4.245 2.20e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.276299   0.056807  -4.864 1.16e-06 ***
CHICAGO_HOME                         0.058188   0.015197   3.829 0.000129 ***
QUAL_LEVELA1 $100M+                  0.625953   0.394281   1.588 0.112398    
QUAL_LEVELA2 $50M - 99.9M            0.338446   0.438767   0.771 0.440505    
QUAL_LEVELA3 $25M - $49.9M           0.086432   0.204667   0.422 0.672808    
QUAL_LEVELA4 $10M - $24.9M           0.613404   0.136053   4.509 6.57e-06 ***
QUAL_LEVELA5 $5M - $9.9M             0.566557   0.100455   5.640 1.73e-08 ***
QUAL_LEVELA6 $2M - $4.9M             0.359408   0.093190   3.857 0.000115 ***
QUAL_LEVELA7 $1M - $1.9M             0.303852   0.073585   4.129 3.66e-05 ***
QUAL_LEVELB  $500K - $999K           0.137050   0.066478   2.062 0.039262 *  
QUAL_LEVELC  $250K - $499K           0.040092   0.062854   0.638 0.523579    
QUAL_LEVELD  $100K - $249K           0.001632   0.061968   0.026 0.978983    
QUAL_LEVELE  $50K - $99K             0.033097   0.063415   0.522 0.601740    
QUAL_LEVELF  $25K - $49K             0.022298   0.063656   0.350 0.726124    
QUAL_LEVELG  $10K - $24K            -0.072604   0.062864  -1.155 0.248133    
QUAL_LEVELH  Under $10K              0.079695   0.218783   0.364 0.715663    
QUAL_LEVELJ  Future Prospect        -0.702462   0.870678  -0.807 0.419793    
AFFINITY_SCORE                       0.083285   0.006525  12.764  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.116635   0.020786   5.611 2.04e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.116749   0.026127   4.468 7.92e-06 ***
MG_PR_MODEL_DESCTop Tier             0.330310   0.026447  12.489  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8677 on 18738 degrees of freedom
Multiple R-squared:  0.5971,    Adjusted R-squared:  0.5962 
F-statistic: 711.9 on 39 and 18738 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1610 -0.6578 -0.0152  0.5677  4.2431 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.054415   0.101893  10.348  < 2e-16 ***
ACTIVE_PROPOSALS                     0.061617   0.030132   2.045 0.040879 *  
ns(NUMERIC_AGE, df = 5)1             0.222418   0.078000   2.852 0.004356 ** 
ns(NUMERIC_AGE, df = 5)2             0.086043   0.091353   0.942 0.346273    
ns(NUMERIC_AGE, df = 5)3            -0.284517   0.062578  -4.547 5.49e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.495247   0.192828   2.568 0.010226 *  
ns(NUMERIC_AGE, df = 5)5            -0.009570   0.102481  -0.093 0.925598    
PM_VISIT_LAST_2_YRS                  0.140860   0.032363   4.353 1.35e-05 ***
log10plus1(VISIT_COUNT)              0.477234   0.027253  17.511  < 2e-16 ***
AF_25K_GIFT                          0.598064   0.039938  14.975  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.439166   0.006163  71.262  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.404708   0.038292  10.569  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.543131   0.023187 -23.424  < 2e-16 ***
MG_250K_PLUS                         1.379719   0.069845  19.754  < 2e-16 ***
PRESIDENT_VISIT                      0.063967   0.057157   1.119 0.263091    
TRUSTEE_OR_ADVISORY_BOARD            0.088990   0.030052   2.961 0.003068 ** 
Alumnus                             -0.649191   0.022011 -29.494  < 2e-16 ***
DOUBLE_ALUM                          0.033747   0.020765   1.625 0.104135    
EVER_PARENT                         -0.087326   0.019958  -4.376 1.22e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.239150   0.055962  -4.273 1.93e-05 ***
CHICAGO_HOME                         0.052681   0.015232   3.459 0.000544 ***
QUAL_LEVELA1 $100M+                  0.144561   0.439016   0.329 0.741945    
QUAL_LEVELA2 $50M - 99.9M            0.527219   0.360149   1.464 0.143241    
QUAL_LEVELA3 $25M - $49.9M           0.041923   0.213887   0.196 0.844609    
QUAL_LEVELA4 $10M - $24.9M           0.501768   0.135263   3.710 0.000208 ***
QUAL_LEVELA5 $5M - $9.9M             0.521536   0.104312   5.000 5.79e-07 ***
QUAL_LEVELA6 $2M - $4.9M             0.306747   0.093858   3.268 0.001084 ** 
QUAL_LEVELA7 $1M - $1.9M             0.284876   0.075140   3.791 0.000150 ***
QUAL_LEVELB  $500K - $999K           0.080796   0.067740   1.193 0.232991    
QUAL_LEVELC  $250K - $499K           0.009287   0.064188   0.145 0.884960    
QUAL_LEVELD  $100K - $249K          -0.051857   0.063318  -0.819 0.412800    
QUAL_LEVELE  $50K - $99K            -0.021342   0.064749  -0.330 0.741705    
QUAL_LEVELF  $25K - $49K            -0.020085   0.064950  -0.309 0.757144    
QUAL_LEVELG  $10K - $24K            -0.103553   0.064209  -1.613 0.106813    
QUAL_LEVELH  Under $10K             -0.072198   0.219189  -0.329 0.741865    
QUAL_LEVELJ  Future Prospect        -0.667151   0.616985  -1.081 0.279574    
AFFINITY_SCORE                       0.084861   0.006535  12.986  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.103630   0.020718   5.002 5.73e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.107867   0.026056   4.140 3.49e-05 ***
MG_PR_MODEL_DESCTop Tier             0.330270   0.026424  12.499  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8673 on 18738 degrees of freedom
Multiple R-squared:  0.5966,    Adjusted R-squared:  0.5957 
F-statistic: 710.5 on 39 and 18738 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1443 -0.6507 -0.0139  0.5688  4.2863 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.018995   0.100703  10.119  < 2e-16 ***
ACTIVE_PROPOSALS                     0.064438   0.029814   2.161 0.030682 *  
ns(NUMERIC_AGE, df = 5)1             0.221519   0.077625   2.854 0.004326 ** 
ns(NUMERIC_AGE, df = 5)2             0.077295   0.090935   0.850 0.395338    
ns(NUMERIC_AGE, df = 5)3            -0.283940   0.062605  -4.535 5.78e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.452125   0.191914   2.356 0.018489 *  
ns(NUMERIC_AGE, df = 5)5            -0.081511   0.103765  -0.786 0.432153    
PM_VISIT_LAST_2_YRS                  0.126050   0.031899   3.952 7.79e-05 ***
log10plus1(VISIT_COUNT)              0.462359   0.027172  17.016  < 2e-16 ***
AF_25K_GIFT                          0.615762   0.039599  15.550  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.444080   0.006154  72.159  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.403265   0.038539  10.464  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.535904   0.023131 -23.169  < 2e-16 ***
MG_250K_PLUS                         1.372640   0.069487  19.754  < 2e-16 ***
PRESIDENT_VISIT                      0.052422   0.056079   0.935 0.349905    
TRUSTEE_OR_ADVISORY_BOARD            0.089180   0.029712   3.001 0.002690 ** 
Alumnus                             -0.654223   0.021953 -29.802  < 2e-16 ***
DOUBLE_ALUM                          0.020569   0.020818   0.988 0.323142    
EVER_PARENT                         -0.078573   0.019965  -3.936 8.33e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.298576   0.055826  -5.348 8.98e-08 ***
CHICAGO_HOME                         0.053221   0.015200   3.501 0.000464 ***
QUAL_LEVELA1 $100M+                  0.706632   0.506194   1.396 0.162740    
QUAL_LEVELA2 $50M - 99.9M            0.588582   0.359949   1.635 0.102028    
QUAL_LEVELA3 $25M - $49.9M           0.045706   0.199717   0.229 0.818984    
QUAL_LEVELA4 $10M - $24.9M           0.490708   0.134087   3.660 0.000253 ***
QUAL_LEVELA5 $5M - $9.9M             0.528860   0.101083   5.232 1.70e-07 ***
QUAL_LEVELA6 $2M - $4.9M             0.399074   0.092886   4.296 1.74e-05 ***
QUAL_LEVELA7 $1M - $1.9M             0.317383   0.074190   4.278 1.90e-05 ***
QUAL_LEVELB  $500K - $999K           0.146891   0.066674   2.203 0.027599 *  
QUAL_LEVELC  $250K - $499K           0.047633   0.063239   0.753 0.451321    
QUAL_LEVELD  $100K - $249K          -0.001135   0.062293  -0.018 0.985458    
QUAL_LEVELE  $50K - $99K             0.038863   0.063738   0.610 0.542043    
QUAL_LEVELF  $25K - $49K             0.040087   0.063968   0.627 0.530880    
QUAL_LEVELG  $10K - $24K            -0.061519   0.063213  -0.973 0.330471    
QUAL_LEVELH  Under $10K             -0.044074   0.198604  -0.222 0.824379    
QUAL_LEVELJ  Future Prospect        -0.599734   0.616840  -0.972 0.330929    
AFFINITY_SCORE                       0.082056   0.006516  12.592  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.094498   0.020826   4.537 5.73e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.095288   0.026207   3.636 0.000278 ***
MG_PR_MODEL_DESCTop Tier             0.336462   0.026394  12.747  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8673 on 18738 degrees of freedom
Multiple R-squared:  0.5984,    Adjusted R-squared:  0.5975 
F-statistic: 715.8 on 39 and 18738 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    ns(NUMERIC_AGE, df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DOUBLE_ALUM + EVER_PARENT + ns(SEASON_TICKET_YEARS, 
    df = 1) + CHICAGO_HOME + QUAL_LEVEL + AFFINITY_SCORE + MG_PR_MODEL_DESC, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1430 -0.6636 -0.0128  0.5684  4.0394 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.984996   0.100246   9.826  < 2e-16 ***
ACTIVE_PROPOSALS                     0.077020   0.030107   2.558 0.010530 *  
ns(NUMERIC_AGE, df = 5)1             0.218233   0.077226   2.826 0.004720 ** 
ns(NUMERIC_AGE, df = 5)2             0.088870   0.090423   0.983 0.325703    
ns(NUMERIC_AGE, df = 5)3            -0.294308   0.062442  -4.713 2.46e-06 ***
ns(NUMERIC_AGE, df = 5)4             0.507612   0.191032   2.657 0.007886 ** 
ns(NUMERIC_AGE, df = 5)5            -0.032575   0.102771  -0.317 0.751269    
PM_VISIT_LAST_2_YRS                  0.123971   0.032109   3.861 0.000113 ***
log10plus1(VISIT_COUNT)              0.464229   0.027202  17.066  < 2e-16 ***
AF_25K_GIFT                          0.627301   0.039498  15.882  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.437671   0.006153  71.131  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.421697   0.038464  10.964  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.525039   0.023153 -22.677  < 2e-16 ***
MG_250K_PLUS                         1.397009   0.069481  20.106  < 2e-16 ***
PRESIDENT_VISIT                      0.042544   0.056856   0.748 0.454300    
TRUSTEE_OR_ADVISORY_BOARD            0.100446   0.029774   3.374 0.000743 ***
Alumnus                             -0.640247   0.021986 -29.121  < 2e-16 ***
DOUBLE_ALUM                          0.012397   0.020834   0.595 0.551824    
EVER_PARENT                         -0.077207   0.019983  -3.864 0.000112 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.279052   0.056435  -4.945 7.69e-07 ***
CHICAGO_HOME                         0.057580   0.015249   3.776 0.000160 ***
QUAL_LEVELA1 $100M+                  0.659487   0.394690   1.671 0.094759 .  
QUAL_LEVELA2 $50M - 99.9M            0.545122   0.393481   1.385 0.165952    
QUAL_LEVELA3 $25M - $49.9M           0.171221   0.199877   0.857 0.391659    
QUAL_LEVELA4 $10M - $24.9M           0.602859   0.134799   4.472 7.78e-06 ***
QUAL_LEVELA5 $5M - $9.9M             0.512281   0.101770   5.034 4.86e-07 ***
QUAL_LEVELA6 $2M - $4.9M             0.348640   0.091927   3.793 0.000150 ***
QUAL_LEVELA7 $1M - $1.9M             0.328067   0.074095   4.428 9.58e-06 ***
QUAL_LEVELB  $500K - $999K           0.136262   0.066492   2.049 0.040449 *  
QUAL_LEVELC  $250K - $499K           0.068478   0.063056   1.086 0.277494    
QUAL_LEVELD  $100K - $249K           0.002294   0.062094   0.037 0.970528    
QUAL_LEVELE  $50K - $99K             0.046585   0.063577   0.733 0.463736    
QUAL_LEVELF  $25K - $49K             0.034613   0.063777   0.543 0.587324    
QUAL_LEVELG  $10K - $24K            -0.054509   0.063017  -0.865 0.387049    
QUAL_LEVELH  Under $10K             -0.041999   0.203290  -0.207 0.836328    
QUAL_LEVELJ  Future Prospect        -0.597773   0.617731  -0.968 0.333211    
AFFINITY_SCORE                       0.084097   0.006525  12.888  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.111407   0.020881   5.335 9.65e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.103621   0.026196   3.956 7.66e-05 ***
MG_PR_MODEL_DESCTop Tier             0.331219   0.026478  12.509  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8686 on 18734 degrees of freedom
Multiple R-squared:  0.5955,    Adjusted R-squared:  0.5946 
F-statistic: 707.1 on 39 and 18734 DF,  p-value: < 2.2e-16

## Transaction all predictors 2 {#appendix-trans-all-2}

Back

lapply(tlmaps2, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8750 -0.6584 -0.0198  0.5747  4.2474 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.106745   0.066358  16.678  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.453626   0.041248 -10.998  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.358644   0.140827   2.547 0.010883 *  
ns(NUMERIC_AGE, df = 3)3             0.026666   0.079578   0.335 0.737563    
PM_VISIT_LAST_2_YRS                  0.192421   0.026059   7.384 1.60e-13 ***
log10plus1(VISIT_COUNT)              0.474494   0.026865  17.662  < 2e-16 ***
AF_25K_GIFT                          0.684029   0.039212  17.444  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.438473   0.006157  71.214  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.422669   0.038528  10.970  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.541653   0.023189 -23.358  < 2e-16 ***
MG_250K_PLUS                         1.602063   0.066422  24.119  < 2e-16 ***
PRESIDENT_VISIT                      0.157782   0.055195   2.859 0.004259 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.102029   0.029641   3.442 0.000578 ***
Alumnus                             -0.644498   0.021767 -29.609  < 2e-16 ***
EVER_PARENT                         -0.076859   0.019829  -3.876 0.000106 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271313   0.056249  -4.823 1.42e-06 ***
CHICAGO_HOME                         0.051622   0.015248   3.386 0.000712 ***
AFFINITY_SCORE                       0.077855   0.006422  12.124  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.098617   0.020053   4.918 8.83e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.097681   0.025684   3.803 0.000143 ***
MG_PR_MODEL_DESCTop Tier             0.370779   0.025548  14.513  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8707 on 18757 degrees of freedom
Multiple R-squared:  0.5928,    Adjusted R-squared:  0.5924 
F-statistic:  1365 on 20 and 18757 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8821 -0.6584 -0.0213  0.5727  4.2596 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.088929   0.066440  16.390  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.441453   0.040704 -10.845  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.375457   0.139812   2.685 0.007250 ** 
ns(NUMERIC_AGE, df = 3)3            -0.004023   0.073173  -0.055 0.956151    
PM_VISIT_LAST_2_YRS                  0.191826   0.026036   7.368 1.81e-13 ***
log10plus1(VISIT_COUNT)              0.452768   0.026840  16.869  < 2e-16 ***
AF_25K_GIFT                          0.645790   0.039735  16.252  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.439840   0.006126  71.797  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.407928   0.038410  10.620  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.558673   0.023165 -24.117  < 2e-16 ***
MG_250K_PLUS                         1.617986   0.064628  25.035  < 2e-16 ***
PRESIDENT_VISIT                      0.164765   0.054930   3.000 0.002708 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.099861   0.029783   3.353 0.000801 ***
Alumnus                             -0.648234   0.021709 -29.861  < 2e-16 ***
EVER_PARENT                         -0.076365   0.019731  -3.870 0.000109 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256688   0.056601  -4.535 5.79e-06 ***
CHICAGO_HOME                         0.050880   0.015188   3.350 0.000810 ***
AFFINITY_SCORE                       0.080413   0.006400  12.565  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.102848   0.019979   5.148 2.66e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.119968   0.025612   4.684 2.83e-06 ***
MG_PR_MODEL_DESCTop Tier             0.402678   0.025612  15.722  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8683 on 18757 degrees of freedom
Multiple R-squared:  0.5968,    Adjusted R-squared:  0.5963 
F-statistic:  1388 on 20 and 18757 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8771 -0.6623 -0.0163  0.5748  4.2590 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.082645   0.067339  16.078  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.449336   0.041525 -10.821  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.377538   0.142841   2.643 0.008223 ** 
ns(NUMERIC_AGE, df = 3)3             0.015328   0.078600   0.195 0.845382    
PM_VISIT_LAST_2_YRS                  0.197041   0.026073   7.557 4.30e-14 ***
log10plus1(VISIT_COUNT)              0.464230   0.026836  17.299  < 2e-16 ***
AF_25K_GIFT                          0.670878   0.039352  17.048  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441327   0.006155  71.707  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.399131   0.038524  10.361  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.555852   0.023280 -23.877  < 2e-16 ***
MG_250K_PLUS                         1.611500   0.065798  24.492  < 2e-16 ***
PRESIDENT_VISIT                      0.141530   0.054885   2.579 0.009926 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.106714   0.029899   3.569 0.000359 ***
Alumnus                             -0.642949   0.021747 -29.565  < 2e-16 ***
EVER_PARENT                         -0.065420   0.019842  -3.297 0.000979 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271906   0.055670  -4.884 1.05e-06 ***
CHICAGO_HOME                         0.053847   0.015299   3.520 0.000433 ***
AFFINITY_SCORE                       0.081961   0.006425  12.757  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.098145   0.020068   4.891 1.01e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.097822   0.025746   3.800 0.000145 ***
MG_PR_MODEL_DESCTop Tier             0.378193   0.025645  14.747  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.872 on 18757 degrees of freedom
Multiple R-squared:  0.5955,    Adjusted R-squared:  0.5951 
F-statistic:  1381 on 20 and 18757 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8916 -0.6551 -0.0172  0.5712  4.2223 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.131272   0.065896  17.168  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.460029   0.041068 -11.202  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.357113   0.139953   2.552 0.010729 *  
ns(NUMERIC_AGE, df = 3)3             0.028585   0.079234   0.361 0.718276    
PM_VISIT_LAST_2_YRS                  0.197504   0.026124   7.560 4.21e-14 ***
log10plus1(VISIT_COUNT)              0.461070   0.026760  17.230  < 2e-16 ***
AF_25K_GIFT                          0.685534   0.039273  17.456  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.436187   0.006143  71.011  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.424618   0.038353  11.071  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.544680   0.023104 -23.575  < 2e-16 ***
MG_250K_PLUS                         1.548409   0.065901  23.496  < 2e-16 ***
PRESIDENT_VISIT                      0.195089   0.055185   3.535 0.000408 ***
TRUSTEE_OR_ADVISORY_BOARD            0.115450   0.029929   3.857 0.000115 ***
Alumnus                             -0.656304   0.021685 -30.265  < 2e-16 ***
EVER_PARENT                         -0.076955   0.019689  -3.908 9.32e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.283895   0.055726  -5.094 3.53e-07 ***
CHICAGO_HOME                         0.053747   0.015229   3.529 0.000418 ***
AFFINITY_SCORE                       0.078956   0.006423  12.293  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.083436   0.020040   4.163 3.15e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.086792   0.025718   3.375 0.000740 ***
MG_PR_MODEL_DESCTop Tier             0.371640   0.025589  14.524  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8695 on 18757 degrees of freedom
Multiple R-squared:  0.593, Adjusted R-squared:  0.5925 
F-statistic:  1366 on 20 and 18757 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8848 -0.6575 -0.0199  0.5743  4.2519 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.0886942  0.0661946  16.447  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.4696901  0.0411656 -11.410  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.3604577  0.1407602   2.561 0.010451 *  
ns(NUMERIC_AGE, df = 3)3             0.0005923  0.0801109   0.007 0.994100    
PM_VISIT_LAST_2_YRS                  0.2087323  0.0262004   7.967 1.72e-15 ***
log10plus1(VISIT_COUNT)              0.4605634  0.0269397  17.096  < 2e-16 ***
AF_25K_GIFT                          0.6901754  0.0394121  17.512  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.4417796  0.0061375  71.981  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.4072144  0.0384912  10.579  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.5532590  0.0232276 -23.819  < 2e-16 ***
MG_250K_PLUS                         1.5738834  0.0659345  23.870  < 2e-16 ***
PRESIDENT_VISIT                      0.1605536  0.0547808   2.931 0.003385 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.1052592  0.0297605   3.537 0.000406 ***
Alumnus                             -0.6531787  0.0217434 -30.040  < 2e-16 ***
EVER_PARENT                         -0.0736060  0.0198622  -3.706 0.000211 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.2840865  0.0565313  -5.025 5.07e-07 ***
CHICAGO_HOME                         0.0563207  0.0152364   3.696 0.000219 ***
AFFINITY_SCORE                       0.0816554  0.0064175  12.724  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.1074746  0.0200802   5.352 8.79e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.0985707  0.0256736   3.839 0.000124 ***
MG_PR_MODEL_DESCTop Tier             0.3775798  0.0255767  14.763  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8713 on 18757 degrees of freedom
Multiple R-squared:  0.5937,    Adjusted R-squared:  0.5933 
F-statistic:  1370 on 20 and 18757 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8802 -0.6590 -0.0181  0.5746  4.2349 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.096866   0.065630  16.713  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.464014   0.041073 -11.297  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.390313   0.139833   2.791 0.005255 ** 
ns(NUMERIC_AGE, df = 3)3             0.025561   0.079495   0.322 0.747807    
PM_VISIT_LAST_2_YRS                  0.197285   0.026368   7.482 7.65e-14 ***
log10plus1(VISIT_COUNT)              0.466281   0.026839  17.373  < 2e-16 ***
AF_25K_GIFT                          0.668930   0.040000  16.723  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.442310   0.006157  71.836  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.419811   0.038558  10.888  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.547008   0.023186 -23.592  < 2e-16 ***
MG_250K_PLUS                         1.610948   0.066919  24.073  < 2e-16 ***
PRESIDENT_VISIT                      0.194161   0.055657   3.489 0.000487 ***
TRUSTEE_OR_ADVISORY_BOARD            0.104165   0.029777   3.498 0.000470 ***
Alumnus                             -0.658298   0.021724 -30.302  < 2e-16 ***
EVER_PARENT                         -0.077621   0.019818  -3.917 9.01e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.287738   0.055938  -5.144 2.72e-07 ***
CHICAGO_HOME                         0.057194   0.015227   3.756 0.000173 ***
AFFINITY_SCORE                       0.079260   0.006430  12.326  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.092727   0.020085   4.617 3.92e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.091485   0.025729   3.556 0.000378 ***
MG_PR_MODEL_DESCTop Tier             0.363516   0.025582  14.210  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8706 on 18757 degrees of freedom
Multiple R-squared:  0.5939,    Adjusted R-squared:  0.5934 
F-statistic:  1371 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8671 -0.6593 -0.0219  0.5725  4.2503 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.104386   0.066531  16.600  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.467928   0.041462 -11.286  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.346309   0.141336   2.450 0.014284 *  
ns(NUMERIC_AGE, df = 3)3            -0.010039   0.079281  -0.127 0.899243    
PM_VISIT_LAST_2_YRS                  0.185758   0.026186   7.094 1.35e-12 ***
log10plus1(VISIT_COUNT)              0.470354   0.026838  17.525  < 2e-16 ***
AF_25K_GIFT                          0.686516   0.039351  17.446  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441887   0.006148  71.875  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.407849   0.038557  10.578  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.542282   0.023231 -23.343  < 2e-16 ***
MG_250K_PLUS                         1.642747   0.066931  24.544  < 2e-16 ***
PRESIDENT_VISIT                      0.199382   0.055200   3.612 0.000305 ***
TRUSTEE_OR_ADVISORY_BOARD            0.109970   0.029659   3.708 0.000210 ***
Alumnus                             -0.652210   0.021829 -29.878  < 2e-16 ***
EVER_PARENT                         -0.079996   0.019914  -4.017 5.92e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.282132   0.057009  -4.949 7.53e-07 ***
CHICAGO_HOME                         0.058069   0.015237   3.811 0.000139 ***
AFFINITY_SCORE                       0.078471   0.006426  12.212  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.102345   0.020093   5.094 3.55e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.103764   0.025750   4.030 5.61e-05 ***
MG_PR_MODEL_DESCTop Tier             0.371402   0.025623  14.495  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8714 on 18757 degrees of freedom
Multiple R-squared:  0.5932,    Adjusted R-squared:  0.5928 
F-statistic:  1368 on 20 and 18757 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8464 -0.6604 -0.0189  0.5745  4.2392 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.078159   0.066803  16.139  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.423016   0.041456 -10.204  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.428920   0.141687   3.027 0.002471 ** 
ns(NUMERIC_AGE, df = 3)3             0.039286   0.078238   0.502 0.615577    
PM_VISIT_LAST_2_YRS                  0.205566   0.026278   7.823 5.44e-15 ***
log10plus1(VISIT_COUNT)              0.470443   0.026910  17.482  < 2e-16 ***
AF_25K_GIFT                          0.653764   0.039774  16.437  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.437396   0.006167  70.929  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.414338   0.038399  10.790  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.546444   0.023254 -23.499  < 2e-16 ***
MG_250K_PLUS                         1.581509   0.066465  23.795  < 2e-16 ***
PRESIDENT_VISIT                      0.180100   0.055670   3.235 0.001218 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.100407   0.030129   3.333 0.000862 ***
Alumnus                             -0.645503   0.021801 -29.609  < 2e-16 ***
EVER_PARENT                         -0.084911   0.019853  -4.277 1.90e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.246271   0.056144  -4.386 1.16e-05 ***
CHICAGO_HOME                         0.052890   0.015269   3.464 0.000534 ***
AFFINITY_SCORE                       0.080414   0.006428  12.511  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.089575   0.020028   4.472 7.78e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.093402   0.025690   3.636 0.000278 ***
MG_PR_MODEL_DESCTop Tier             0.368437   0.025569  14.409  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8709 on 18757 degrees of freedom
Multiple R-squared:  0.5928,    Adjusted R-squared:  0.5924 
F-statistic:  1365 on 20 and 18757 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8859 -0.6590 -0.0183  0.5733  4.2352 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.074699   0.066757  16.099  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.421436   0.041473 -10.162  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.427513   0.141851   3.014 0.002583 ** 
ns(NUMERIC_AGE, df = 3)3            -0.001252   0.079080  -0.016 0.987367    
PM_VISIT_LAST_2_YRS                  0.190448   0.026024   7.318 2.61e-13 ***
log10plus1(VISIT_COUNT)              0.457093   0.026824  17.040  < 2e-16 ***
AF_25K_GIFT                          0.664158   0.039490  16.818  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441899   0.006158  71.756  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.409312   0.038655  10.589  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.537054   0.023202 -23.147  < 2e-16 ***
MG_250K_PLUS                         1.568594   0.066087  23.735  < 2e-16 ***
PRESIDENT_VISIT                      0.157604   0.054862   2.873 0.004074 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.100599   0.029793   3.377 0.000735 ***
Alumnus                             -0.651380   0.021727 -29.981  < 2e-16 ***
EVER_PARENT                         -0.077095   0.019867  -3.881 0.000105 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.301643   0.056025  -5.384 7.37e-08 ***
CHICAGO_HOME                         0.051820   0.015244   3.399 0.000677 ***
AFFINITY_SCORE                       0.078045   0.006417  12.163  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.084092   0.020122   4.179 2.94e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.083721   0.025840   3.240 0.001198 ** 
MG_PR_MODEL_DESCTop Tier             0.379042   0.025543  14.840  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.871 on 18757 degrees of freedom
Multiple R-squared:  0.5945,    Adjusted R-squared:  0.5941 
F-statistic:  1375 on 20 and 18757 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8682 -0.6606 -0.0198  0.5756  3.9778 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.069894   0.066558  16.075  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.435309   0.041423 -10.509  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.425129   0.140997   3.015 0.002572 ** 
ns(NUMERIC_AGE, df = 3)3             0.033406   0.078366   0.426 0.669909    
PM_VISIT_LAST_2_YRS                  0.196509   0.026085   7.533 5.17e-14 ***
log10plus1(VISIT_COUNT)              0.457990   0.026868  17.046  < 2e-16 ***
AF_25K_GIFT                          0.684323   0.039335  17.397  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.436112   0.006157  70.827  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.429306   0.038571  11.130  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.526257   0.023222 -22.662  < 2e-16 ***
MG_250K_PLUS                         1.588142   0.066259  23.969  < 2e-16 ***
PRESIDENT_VISIT                      0.154460   0.055471   2.785 0.005366 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.110727   0.029848   3.710 0.000208 ***
Alumnus                             -0.639238   0.021786 -29.341  < 2e-16 ***
EVER_PARENT                         -0.074822   0.019873  -3.765 0.000167 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.280701   0.056627  -4.957 7.22e-07 ***
CHICAGO_HOME                         0.056929   0.015290   3.723 0.000197 ***
AFFINITY_SCORE                       0.078509   0.006421  12.228  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.099088   0.020160   4.915 8.95e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.091319   0.025805   3.539 0.000403 ***
MG_PR_MODEL_DESCTop Tier             0.375015   0.025618  14.639  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8722 on 18753 degrees of freedom
Multiple R-squared:  0.5917,    Adjusted R-squared:  0.5912 
F-statistic:  1359 on 20 and 18753 DF,  p-value: < 2.2e-16

Transaction all predictors 2

Back

lapply(tlmaps2, function(x) summary(x))

[[1]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8750 -0.6584 -0.0198  0.5747  4.2474 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.106745   0.066358  16.678  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.453626   0.041248 -10.998  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.358644   0.140827   2.547 0.010883 *  
ns(NUMERIC_AGE, df = 3)3             0.026666   0.079578   0.335 0.737563    
PM_VISIT_LAST_2_YRS                  0.192421   0.026059   7.384 1.60e-13 ***
log10plus1(VISIT_COUNT)              0.474494   0.026865  17.662  < 2e-16 ***
AF_25K_GIFT                          0.684029   0.039212  17.444  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.438473   0.006157  71.214  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.422669   0.038528  10.970  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.541653   0.023189 -23.358  < 2e-16 ***
MG_250K_PLUS                         1.602063   0.066422  24.119  < 2e-16 ***
PRESIDENT_VISIT                      0.157782   0.055195   2.859 0.004259 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.102029   0.029641   3.442 0.000578 ***
Alumnus                             -0.644498   0.021767 -29.609  < 2e-16 ***
EVER_PARENT                         -0.076859   0.019829  -3.876 0.000106 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271313   0.056249  -4.823 1.42e-06 ***
CHICAGO_HOME                         0.051622   0.015248   3.386 0.000712 ***
AFFINITY_SCORE                       0.077855   0.006422  12.124  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.098617   0.020053   4.918 8.83e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.097681   0.025684   3.803 0.000143 ***
MG_PR_MODEL_DESCTop Tier             0.370779   0.025548  14.513  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8707 on 18757 degrees of freedom
Multiple R-squared:  0.5928,    Adjusted R-squared:  0.5924 
F-statistic:  1365 on 20 and 18757 DF,  p-value: < 2.2e-16


[[2]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8821 -0.6584 -0.0213  0.5727  4.2596 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.088929   0.066440  16.390  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.441453   0.040704 -10.845  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.375457   0.139812   2.685 0.007250 ** 
ns(NUMERIC_AGE, df = 3)3            -0.004023   0.073173  -0.055 0.956151    
PM_VISIT_LAST_2_YRS                  0.191826   0.026036   7.368 1.81e-13 ***
log10plus1(VISIT_COUNT)              0.452768   0.026840  16.869  < 2e-16 ***
AF_25K_GIFT                          0.645790   0.039735  16.252  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.439840   0.006126  71.797  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.407928   0.038410  10.620  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.558673   0.023165 -24.117  < 2e-16 ***
MG_250K_PLUS                         1.617986   0.064628  25.035  < 2e-16 ***
PRESIDENT_VISIT                      0.164765   0.054930   3.000 0.002708 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.099861   0.029783   3.353 0.000801 ***
Alumnus                             -0.648234   0.021709 -29.861  < 2e-16 ***
EVER_PARENT                         -0.076365   0.019731  -3.870 0.000109 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.256688   0.056601  -4.535 5.79e-06 ***
CHICAGO_HOME                         0.050880   0.015188   3.350 0.000810 ***
AFFINITY_SCORE                       0.080413   0.006400  12.565  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.102848   0.019979   5.148 2.66e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.119968   0.025612   4.684 2.83e-06 ***
MG_PR_MODEL_DESCTop Tier             0.402678   0.025612  15.722  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8683 on 18757 degrees of freedom
Multiple R-squared:  0.5968,    Adjusted R-squared:  0.5963 
F-statistic:  1388 on 20 and 18757 DF,  p-value: < 2.2e-16


[[3]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8771 -0.6623 -0.0163  0.5748  4.2590 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.082645   0.067339  16.078  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.449336   0.041525 -10.821  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.377538   0.142841   2.643 0.008223 ** 
ns(NUMERIC_AGE, df = 3)3             0.015328   0.078600   0.195 0.845382    
PM_VISIT_LAST_2_YRS                  0.197041   0.026073   7.557 4.30e-14 ***
log10plus1(VISIT_COUNT)              0.464230   0.026836  17.299  < 2e-16 ***
AF_25K_GIFT                          0.670878   0.039352  17.048  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441327   0.006155  71.707  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.399131   0.038524  10.361  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.555852   0.023280 -23.877  < 2e-16 ***
MG_250K_PLUS                         1.611500   0.065798  24.492  < 2e-16 ***
PRESIDENT_VISIT                      0.141530   0.054885   2.579 0.009926 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.106714   0.029899   3.569 0.000359 ***
Alumnus                             -0.642949   0.021747 -29.565  < 2e-16 ***
EVER_PARENT                         -0.065420   0.019842  -3.297 0.000979 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.271906   0.055670  -4.884 1.05e-06 ***
CHICAGO_HOME                         0.053847   0.015299   3.520 0.000433 ***
AFFINITY_SCORE                       0.081961   0.006425  12.757  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.098145   0.020068   4.891 1.01e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.097822   0.025746   3.800 0.000145 ***
MG_PR_MODEL_DESCTop Tier             0.378193   0.025645  14.747  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.872 on 18757 degrees of freedom
Multiple R-squared:  0.5955,    Adjusted R-squared:  0.5951 
F-statistic:  1381 on 20 and 18757 DF,  p-value: < 2.2e-16


[[4]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8916 -0.6551 -0.0172  0.5712  4.2223 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.131272   0.065896  17.168  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.460029   0.041068 -11.202  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.357113   0.139953   2.552 0.010729 *  
ns(NUMERIC_AGE, df = 3)3             0.028585   0.079234   0.361 0.718276    
PM_VISIT_LAST_2_YRS                  0.197504   0.026124   7.560 4.21e-14 ***
log10plus1(VISIT_COUNT)              0.461070   0.026760  17.230  < 2e-16 ***
AF_25K_GIFT                          0.685534   0.039273  17.456  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.436187   0.006143  71.011  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.424618   0.038353  11.071  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.544680   0.023104 -23.575  < 2e-16 ***
MG_250K_PLUS                         1.548409   0.065901  23.496  < 2e-16 ***
PRESIDENT_VISIT                      0.195089   0.055185   3.535 0.000408 ***
TRUSTEE_OR_ADVISORY_BOARD            0.115450   0.029929   3.857 0.000115 ***
Alumnus                             -0.656304   0.021685 -30.265  < 2e-16 ***
EVER_PARENT                         -0.076955   0.019689  -3.908 9.32e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.283895   0.055726  -5.094 3.53e-07 ***
CHICAGO_HOME                         0.053747   0.015229   3.529 0.000418 ***
AFFINITY_SCORE                       0.078956   0.006423  12.293  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.083436   0.020040   4.163 3.15e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.086792   0.025718   3.375 0.000740 ***
MG_PR_MODEL_DESCTop Tier             0.371640   0.025589  14.524  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8695 on 18757 degrees of freedom
Multiple R-squared:  0.593, Adjusted R-squared:  0.5925 
F-statistic:  1366 on 20 and 18757 DF,  p-value: < 2.2e-16


[[5]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8848 -0.6575 -0.0199  0.5743  4.2519 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.0886942  0.0661946  16.447  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.4696901  0.0411656 -11.410  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.3604577  0.1407602   2.561 0.010451 *  
ns(NUMERIC_AGE, df = 3)3             0.0005923  0.0801109   0.007 0.994100    
PM_VISIT_LAST_2_YRS                  0.2087323  0.0262004   7.967 1.72e-15 ***
log10plus1(VISIT_COUNT)              0.4605634  0.0269397  17.096  < 2e-16 ***
AF_25K_GIFT                          0.6901754  0.0394121  17.512  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.4417796  0.0061375  71.981  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.4072144  0.0384912  10.579  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.5532590  0.0232276 -23.819  < 2e-16 ***
MG_250K_PLUS                         1.5738834  0.0659345  23.870  < 2e-16 ***
PRESIDENT_VISIT                      0.1605536  0.0547808   2.931 0.003385 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.1052592  0.0297605   3.537 0.000406 ***
Alumnus                             -0.6531787  0.0217434 -30.040  < 2e-16 ***
EVER_PARENT                         -0.0736060  0.0198622  -3.706 0.000211 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.2840865  0.0565313  -5.025 5.07e-07 ***
CHICAGO_HOME                         0.0563207  0.0152364   3.696 0.000219 ***
AFFINITY_SCORE                       0.0816554  0.0064175  12.724  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.1074746  0.0200802   5.352 8.79e-08 ***
MG_PR_MODEL_DESCMiddle Tier          0.0985707  0.0256736   3.839 0.000124 ***
MG_PR_MODEL_DESCTop Tier             0.3775798  0.0255767  14.763  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8713 on 18757 degrees of freedom
Multiple R-squared:  0.5937,    Adjusted R-squared:  0.5933 
F-statistic:  1370 on 20 and 18757 DF,  p-value: < 2.2e-16


[[6]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8802 -0.6590 -0.0181  0.5746  4.2349 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.096866   0.065630  16.713  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.464014   0.041073 -11.297  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.390313   0.139833   2.791 0.005255 ** 
ns(NUMERIC_AGE, df = 3)3             0.025561   0.079495   0.322 0.747807    
PM_VISIT_LAST_2_YRS                  0.197285   0.026368   7.482 7.65e-14 ***
log10plus1(VISIT_COUNT)              0.466281   0.026839  17.373  < 2e-16 ***
AF_25K_GIFT                          0.668930   0.040000  16.723  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.442310   0.006157  71.836  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.419811   0.038558  10.888  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.547008   0.023186 -23.592  < 2e-16 ***
MG_250K_PLUS                         1.610948   0.066919  24.073  < 2e-16 ***
PRESIDENT_VISIT                      0.194161   0.055657   3.489 0.000487 ***
TRUSTEE_OR_ADVISORY_BOARD            0.104165   0.029777   3.498 0.000470 ***
Alumnus                             -0.658298   0.021724 -30.302  < 2e-16 ***
EVER_PARENT                         -0.077621   0.019818  -3.917 9.01e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.287738   0.055938  -5.144 2.72e-07 ***
CHICAGO_HOME                         0.057194   0.015227   3.756 0.000173 ***
AFFINITY_SCORE                       0.079260   0.006430  12.326  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.092727   0.020085   4.617 3.92e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.091485   0.025729   3.556 0.000378 ***
MG_PR_MODEL_DESCTop Tier             0.363516   0.025582  14.210  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8706 on 18757 degrees of freedom
Multiple R-squared:  0.5939,    Adjusted R-squared:  0.5934 
F-statistic:  1371 on 20 and 18757 DF,  p-value: < 2.2e-16


[[7]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8671 -0.6593 -0.0219  0.5725  4.2503 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.104386   0.066531  16.600  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.467928   0.041462 -11.286  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.346309   0.141336   2.450 0.014284 *  
ns(NUMERIC_AGE, df = 3)3            -0.010039   0.079281  -0.127 0.899243    
PM_VISIT_LAST_2_YRS                  0.185758   0.026186   7.094 1.35e-12 ***
log10plus1(VISIT_COUNT)              0.470354   0.026838  17.525  < 2e-16 ***
AF_25K_GIFT                          0.686516   0.039351  17.446  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441887   0.006148  71.875  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.407849   0.038557  10.578  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.542282   0.023231 -23.343  < 2e-16 ***
MG_250K_PLUS                         1.642747   0.066931  24.544  < 2e-16 ***
PRESIDENT_VISIT                      0.199382   0.055200   3.612 0.000305 ***
TRUSTEE_OR_ADVISORY_BOARD            0.109970   0.029659   3.708 0.000210 ***
Alumnus                             -0.652210   0.021829 -29.878  < 2e-16 ***
EVER_PARENT                         -0.079996   0.019914  -4.017 5.92e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.282132   0.057009  -4.949 7.53e-07 ***
CHICAGO_HOME                         0.058069   0.015237   3.811 0.000139 ***
AFFINITY_SCORE                       0.078471   0.006426  12.212  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.102345   0.020093   5.094 3.55e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.103764   0.025750   4.030 5.61e-05 ***
MG_PR_MODEL_DESCTop Tier             0.371402   0.025623  14.495  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8714 on 18757 degrees of freedom
Multiple R-squared:  0.5932,    Adjusted R-squared:  0.5928 
F-statistic:  1368 on 20 and 18757 DF,  p-value: < 2.2e-16


[[8]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8464 -0.6604 -0.0189  0.5745  4.2392 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.078159   0.066803  16.139  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.423016   0.041456 -10.204  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.428920   0.141687   3.027 0.002471 ** 
ns(NUMERIC_AGE, df = 3)3             0.039286   0.078238   0.502 0.615577    
PM_VISIT_LAST_2_YRS                  0.205566   0.026278   7.823 5.44e-15 ***
log10plus1(VISIT_COUNT)              0.470443   0.026910  17.482  < 2e-16 ***
AF_25K_GIFT                          0.653764   0.039774  16.437  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.437396   0.006167  70.929  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.414338   0.038399  10.790  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.546444   0.023254 -23.499  < 2e-16 ***
MG_250K_PLUS                         1.581509   0.066465  23.795  < 2e-16 ***
PRESIDENT_VISIT                      0.180100   0.055670   3.235 0.001218 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.100407   0.030129   3.333 0.000862 ***
Alumnus                             -0.645503   0.021801 -29.609  < 2e-16 ***
EVER_PARENT                         -0.084911   0.019853  -4.277 1.90e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.246271   0.056144  -4.386 1.16e-05 ***
CHICAGO_HOME                         0.052890   0.015269   3.464 0.000534 ***
AFFINITY_SCORE                       0.080414   0.006428  12.511  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.089575   0.020028   4.472 7.78e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.093402   0.025690   3.636 0.000278 ***
MG_PR_MODEL_DESCTop Tier             0.368437   0.025569  14.409  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8709 on 18757 degrees of freedom
Multiple R-squared:  0.5928,    Adjusted R-squared:  0.5924 
F-statistic:  1365 on 20 and 18757 DF,  p-value: < 2.2e-16


[[9]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8859 -0.6590 -0.0183  0.5733  4.2352 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.074699   0.066757  16.099  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.421436   0.041473 -10.162  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.427513   0.141851   3.014 0.002583 ** 
ns(NUMERIC_AGE, df = 3)3            -0.001252   0.079080  -0.016 0.987367    
PM_VISIT_LAST_2_YRS                  0.190448   0.026024   7.318 2.61e-13 ***
log10plus1(VISIT_COUNT)              0.457093   0.026824  17.040  < 2e-16 ***
AF_25K_GIFT                          0.664158   0.039490  16.818  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.441899   0.006158  71.756  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.409312   0.038655  10.589  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.537054   0.023202 -23.147  < 2e-16 ***
MG_250K_PLUS                         1.568594   0.066087  23.735  < 2e-16 ***
PRESIDENT_VISIT                      0.157604   0.054862   2.873 0.004074 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.100599   0.029793   3.377 0.000735 ***
Alumnus                             -0.651380   0.021727 -29.981  < 2e-16 ***
EVER_PARENT                         -0.077095   0.019867  -3.881 0.000105 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.301643   0.056025  -5.384 7.37e-08 ***
CHICAGO_HOME                         0.051820   0.015244   3.399 0.000677 ***
AFFINITY_SCORE                       0.078045   0.006417  12.163  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.084092   0.020122   4.179 2.94e-05 ***
MG_PR_MODEL_DESCMiddle Tier          0.083721   0.025840   3.240 0.001198 ** 
MG_PR_MODEL_DESCTop Tier             0.379042   0.025543  14.840  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.871 on 18757 degrees of freedom
Multiple R-squared:  0.5945,    Adjusted R-squared:  0.5941 
F-statistic:  1375 on 20 and 18757 DF,  p-value: < 2.2e-16


[[10]]

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8682 -0.6606 -0.0198  0.5756  3.9778 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.069894   0.066558  16.075  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.435309   0.041423 -10.509  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.425129   0.140997   3.015 0.002572 ** 
ns(NUMERIC_AGE, df = 3)3             0.033406   0.078366   0.426 0.669909    
PM_VISIT_LAST_2_YRS                  0.196509   0.026085   7.533 5.17e-14 ***
log10plus1(VISIT_COUNT)              0.457990   0.026868  17.046  < 2e-16 ***
AF_25K_GIFT                          0.684323   0.039335  17.397  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.436112   0.006157  70.827  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.429306   0.038571  11.130  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.526257   0.023222 -22.662  < 2e-16 ***
MG_250K_PLUS                         1.588142   0.066259  23.969  < 2e-16 ***
PRESIDENT_VISIT                      0.154460   0.055471   2.785 0.005366 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.110727   0.029848   3.710 0.000208 ***
Alumnus                             -0.639238   0.021786 -29.341  < 2e-16 ***
EVER_PARENT                         -0.074822   0.019873  -3.765 0.000167 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.280701   0.056627  -4.957 7.22e-07 ***
CHICAGO_HOME                         0.056929   0.015290   3.723 0.000197 ***
AFFINITY_SCORE                       0.078509   0.006421  12.228  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.099088   0.020160   4.915 8.95e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.091319   0.025805   3.539 0.000403 ***
MG_PR_MODEL_DESCTop Tier             0.375015   0.025618  14.639  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8722 on 18753 degrees of freedom
Multiple R-squared:  0.5917,    Adjusted R-squared:  0.5912 
F-statistic:  1359 on 20 and 18753 DF,  p-value: < 2.2e-16

Transaction final models

Back

summary(tlm_final)

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = mdat %>% 
    filter(rownum %in% unlist(xval_inds)))

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3394 -0.5624  0.1473  0.7490  3.8297 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.76196    0.02216  79.521  < 2e-16 ***
ACTIVE_PROPOSALS           0.22505    0.03617   6.223 4.98e-10 ***
AGE                        0.28640    0.01692  16.930  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.26428    0.03820   6.918 4.72e-12 ***
VISITS_5PLUS               0.72417    0.02747  26.363  < 2e-16 ***
AF_25K_GIFT                0.86000    0.04781  17.987  < 2e-16 ***
GAVE_IN_LAST_3_YRS         0.78335    0.01843  42.505  < 2e-16 ***
MG_250K_PLUS               1.76246    0.08019  21.978  < 2e-16 ***
PRESIDENT_VISIT            0.17698    0.06682   2.649  0.00808 ** 
TRUSTEE_OR_ADVISORY_BOARD  0.35546    0.03583   9.921  < 2e-16 ***
Alumnus                    0.02143    0.02184   0.981  0.32654    
DEEP_ENGAGEMENT            0.18696    0.01813  10.311  < 2e-16 ***
CHICAGO_HOME               0.13071    0.01762   7.419 1.22e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.118 on 20851 degrees of freedom
Multiple R-squared:  0.3306,    Adjusted R-squared:  0.3302 
F-statistic: 858.1 on 12 and 20851 DF,  p-value: < 2.2e-16

summary(tlmap_final)

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = mdat %>% 
    filter(rownum %in% unlist(xval_inds)))

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8816 -0.6585 -0.0194  0.5739  4.2452 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.086418   0.063348  17.150  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.445402   0.039330 -11.325  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.399040   0.134481   2.967 0.003008 ** 
ns(NUMERIC_AGE, df = 3)3             0.017137   0.074762   0.229 0.818700    
PM_VISIT_LAST_2_YRS                  0.196261   0.024799   7.914 2.61e-15 ***
log10plus1(VISIT_COUNT)              0.463528   0.025472  18.198  < 2e-16 ***
AF_25K_GIFT                          0.673509   0.037458  17.980  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.439683   0.005834  75.363  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.414306   0.036526  11.343  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.545260   0.022014 -24.769  < 2e-16 ***
MG_250K_PLUS                         1.594283   0.062723  25.418  < 2e-16 ***
PRESIDENT_VISIT                      0.170416   0.052339   3.256 0.001132 ** 
TRUSTEE_OR_ADVISORY_BOARD            0.105537   0.028287   3.731 0.000191 ***
Alumnus                             -0.649499   0.020636 -31.474  < 2e-16 ***
EVER_PARENT                         -0.076141   0.018809  -4.048 5.18e-05 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.276651   0.053355  -5.185 2.18e-07 ***
CHICAGO_HOME                         0.054331   0.014463   3.756 0.000173 ***
AFFINITY_SCORE                       0.079521   0.006091  13.056  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.095847   0.019039   5.034 4.84e-07 ***
MG_PR_MODEL_DESCMiddle Tier          0.096496   0.024403   3.954 7.70e-05 ***
MG_PR_MODEL_DESCTop Tier             0.375853   0.024275  15.483  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8708 on 20843 degrees of freedom
Multiple R-squared:  0.5938,    Adjusted R-squared:  0.5934 
F-statistic:  1523 on 20 and 20843 DF,  p-value: < 2.2e-16

Final comparison {##appendix-final-comparison}

Back

lapply(final_models, function(x) summary(x))

$`campaign.pg.score`

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = mdat %>% 
    filter(rownum %nin% outliers))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8553 -1.2313 -0.1431  0.9948  5.6921 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.37197    0.02383  57.572  < 2e-16 ***
ACTIVE_PROPOSALS           0.27887    0.03914   7.125 1.07e-12 ***
AGE                       -0.22446    0.01815 -12.364  < 2e-16 ***
PM_VISIT_LAST_2_YRS        0.63584    0.04116  15.446  < 2e-16 ***
VISITS_5PLUS               0.72901    0.02954  24.675  < 2e-16 ***
AF_25K_GIFT                0.84388    0.05187  16.270  < 2e-16 ***
GAVE_IN_LAST_3_YRS         2.05061    0.01977 103.736  < 2e-16 ***
MG_250K_PLUS               1.04508    0.08572  12.191  < 2e-16 ***
PRESIDENT_VISIT            0.47405    0.07277   6.514 7.44e-11 ***
TRUSTEE_OR_ADVISORY_BOARD  0.27974    0.03841   7.282 3.38e-13 ***
Alumnus                   -0.14066    0.02353  -5.979 2.28e-09 ***
DEEP_ENGAGEMENT            0.31504    0.01946  16.190  < 2e-16 ***
CHICAGO_HOME               0.10443    0.01896   5.507 3.68e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 26066 degrees of freedom
Multiple R-squared:  0.4644,    Adjusted R-squared:  0.4641 
F-statistic:  1883 on 12 and 26066 DF,  p-value: < 2.2e-16


$largest.trans.pg.score

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ACTIVE_PROPOSALS + 
    AGE + PM_VISIT_LAST_2_YRS + VISITS_5PLUS + AF_25K_GIFT + 
    GAVE_IN_LAST_3_YRS + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + DEEP_ENGAGEMENT + CHICAGO_HOME, data = mdat %>% 
    filter(rownum %nin% outliers))

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6439 -0.5621  0.1373  0.7348  4.2262 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               1.777602   0.019786  89.843  < 2e-16 ***
ACTIVE_PROPOSALS          0.214641   0.032496   6.605 4.05e-11 ***
AGE                       0.296701   0.015073  19.685  < 2e-16 ***
PM_VISIT_LAST_2_YRS       0.286346   0.034178   8.378  < 2e-16 ***
VISITS_5PLUS              0.703268   0.024530  28.670  < 2e-16 ***
AF_25K_GIFT               0.870448   0.043064  20.213  < 2e-16 ***
GAVE_IN_LAST_3_YRS        0.781127   0.016413  47.593  < 2e-16 ***
MG_250K_PLUS              1.729127   0.071174  24.294  < 2e-16 ***
PRESIDENT_VISIT           0.141862   0.060419   2.348   0.0189 *  
TRUSTEE_OR_ADVISORY_BOARD 0.359298   0.031894  11.265  < 2e-16 ***
Alumnus                   0.007669   0.019534   0.393   0.6946    
DEEP_ENGAGEMENT           0.187769   0.016156  11.622  < 2e-16 ***
CHICAGO_HOME              0.137207   0.015745   8.715  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.116 on 26066 degrees of freedom
Multiple R-squared:  0.3279,    Adjusted R-squared:  0.3275 
F-statistic:  1060 on 12 and 26066 DF,  p-value: < 2.2e-16


$campaign.all.preds

Call:
lm(formula = log10plus1(CAMPAIGN_NEWGIFT_CMIT_CREDIT) ~ ns(NUMERIC_AGE, 
    df = 5) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + Alumnus + ns(SEASON_TICKET_YEARS, 
    df = 1) + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = mdat %>% 
    filter(rownum %nin% outliers))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3893 -0.6200 -0.1801  0.4947  6.2635 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.226749   0.091867   2.468 0.013585 *  
ns(NUMERIC_AGE, df = 5)1             0.431494   0.084430   5.111 3.23e-07 ***
ns(NUMERIC_AGE, df = 5)2             0.311180   0.097686   3.186 0.001447 ** 
ns(NUMERIC_AGE, df = 5)3            -0.380150   0.062292  -6.103 1.06e-09 ***
ns(NUMERIC_AGE, df = 5)4             0.611299   0.206104   2.966 0.003020 ** 
ns(NUMERIC_AGE, df = 5)5            -0.362376   0.098792  -3.668 0.000245 ***
PM_VISIT_LAST_2_YRS                  0.324460   0.024558  13.212  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.294325   0.024569  11.979  < 2e-16 ***
AF_25K_GIFT                          0.609888   0.036933  16.513  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.261825   0.005764  45.425  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  2.917451   0.035980  81.085  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2  0.574423   0.021716  26.452  < 2e-16 ***
MG_250K_PLUS                         1.062765   0.060285  17.629  < 2e-16 ***
Alumnus                             -0.573615   0.018229 -31.467  < 2e-16 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.295534   0.056579  -5.223 1.77e-07 ***
AFFINITY_SCORE                       0.155712   0.005786  26.913  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier         -0.134420   0.018888  -7.117 1.13e-12 ***
MG_PR_MODEL_DESCMiddle Tier          0.181198   0.024139   7.507 6.26e-14 ***
MG_PR_MODEL_DESCTop Tier             0.562016   0.024047  23.372  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9654 on 26060 degrees of freedom
Multiple R-squared:  0.7235,    Adjusted R-squared:  0.7233 
F-statistic:  3789 on 18 and 26060 DF,  p-value: < 2.2e-16


$largest.trans.all.preds

Call:
lm(formula = log10plus1(LARGEST_GIFT_OR_PAYMENT) ~ ns(NUMERIC_AGE, 
    df = 3) + PM_VISIT_LAST_2_YRS + log10plus1(VISIT_COUNT) + 
    AF_25K_GIFT + sqrt(YEARS_OF_GIVING) + ns(YEARS_OF_GIVING_LAST_3, 
    df = 2) + MG_250K_PLUS + PRESIDENT_VISIT + TRUSTEE_OR_ADVISORY_BOARD + 
    Alumnus + EVER_PARENT + ns(SEASON_TICKET_YEARS, df = 1) + 
    CHICAGO_HOME + AFFINITY_SCORE + MG_PR_MODEL_DESC, data = mdat %>% 
    filter(rownum %nin% outliers))

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7286 -0.6589 -0.0182  0.5716  4.5864 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          1.056472   0.063350  16.677  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)1            -0.386585   0.036663 -10.544  < 2e-16 ***
ns(NUMERIC_AGE, df = 3)2             0.507589   0.134228   3.782 0.000156 ***
ns(NUMERIC_AGE, df = 3)3             0.052208   0.068724   0.760 0.447452    
PM_VISIT_LAST_2_YRS                  0.206842   0.022187   9.323  < 2e-16 ***
log10plus1(VISIT_COUNT)              0.465506   0.022757  20.456  < 2e-16 ***
AF_25K_GIFT                          0.669998   0.033839  19.799  < 2e-16 ***
sqrt(YEARS_OF_GIVING)                0.435550   0.005218  83.473  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)1  0.414493   0.032630  12.703  < 2e-16 ***
ns(YEARS_OF_GIVING_LAST_3, df = 2)2 -0.538414   0.019672 -27.370  < 2e-16 ***
MG_250K_PLUS                         1.571744   0.055798  28.169  < 2e-16 ***
PRESIDENT_VISIT                      0.113327   0.047447   2.389 0.016923 *  
TRUSTEE_OR_ADVISORY_BOARD            0.114033   0.025205   4.524 6.09e-06 ***
Alumnus                             -0.649690   0.018441 -35.232  < 2e-16 ***
EVER_PARENT                         -0.075523   0.016798  -4.496 6.96e-06 ***
ns(SEASON_TICKET_YEARS, df = 1)     -0.315162   0.051835  -6.080 1.22e-09 ***
CHICAGO_HOME                         0.064236   0.012957   4.958 7.18e-07 ***
AFFINITY_SCORE                       0.079900   0.005440  14.688  < 2e-16 ***
MG_PR_MODEL_DESCBottom Tier          0.080314   0.017056   4.709 2.51e-06 ***
MG_PR_MODEL_DESCMiddle Tier          0.075397   0.021789   3.460 0.000540 ***
MG_PR_MODEL_DESCTop Tier             0.355578   0.021708  16.380  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8714 on 26058 degrees of freedom
Multiple R-squared:  0.5899,    Adjusted R-squared:  0.5896 
F-statistic:  1874 on 20 and 26058 DF,  p-value: < 2.2e-16